The segmentation of blood vessels through color fundus images is a difficult and time-consuming task that requires experienced clinicians. Recently, researchers have shown that blood vessel segmentation using methods based on deep neural networks has achieved highly satisfactory results. This motivates us to employ a fast and accurate deep learning-based method that can be used in blood vessel segmentation. Five improved deep learning-based networks (U-Net, DenseU-Net, LadderNet, R2U-Net, and ATTU-Net), along with an enhanced customized R2-ATT U-Net deep learning network, have been employed to segment the retinal blood vessel tree. Segmentation using patches extraction is executed, where we have used 10,000 patches per image, in total, 8000 for training and 2000 for testing public benchmark STARE dataset images. Initially, we performed the training, followed by testing for all six models using the patch extraction approach. All the aspects of the testing phase (test log, ROC curve, precision recall curves, and confusion matrix) and statistical performance measuring metrics (accuracy, sensitivity, specificity, F1 score, precision, and AUC values) are covered in this work and are also shown in the form of tables and graphs. An in-depth performance evaluation analysis of these six implemented improved nets has been performed to evaluate the segmentation process. We got the best result in the case of LadderNet, with an accuracy of 0.971, which is comparable to recent state-of-the-art studies. In terms of accuracy, the enhanced customized R2-ATT U-Net deep learning network is also highly satisfactory, being near the best model, LadderNet. The experiment results demonstrate that the proposed methodology achieves satisfactory performance in the retinal blood vessel extraction domain and can help ophthalmologists predict many eye-related diseases at a preliminary stage.

The timely identification of COVID-19 is essential to mitigate elevated mortality rates and prevent the future proliferation of the pandemic. Diagnostic test kits identify the illness; however, they often require considerable time and may present challenges regarding accuracy. Chest computed tomography (CT) testing demonstrates greater accuracy and can also serve as a diagnostic tool for COVID-19. This empirical investigation utilizes a three-step innovative and highly effective hybrid methodology that integrates image processing, soft computing, and machine learning techniques to detect COVID-19 from chest CT scans. Following the pre-processing of the chest CT images, we proceeded to extract 213 features categorized into various classes. During the second phase, the selection of the most informative features essential for prediction was conducted utilizing three soft computing algorithms: the grey wolf optimization algorithm (GWO), the salp swarm-based optimization algorithm (SSA), and a proposed hybrid approach combining both algorithms. Leveraging the features identified through soft computing algorithms, the five benchmark machine learning models were trained and utilized to classify these CT images into COVID-19 and non-COVID categories based on the chosen feature set. We undertook comprehensive testing involving 24 distinct tests (utilizing both 5-fold and 10-fold cross-validation approach). For each test, we evaluated performance across nine different standard metrics. The proposed model underwent evaluation using a publicly available CT dataset, achieving an impressive AUC of 0.9999 and a notable maximum accuracy rate of 96.90

Social networks are experiencing a significant surge in the demand for text mining. A growing number of persons are participating in online text analysis. As Facebook and Twitter gain popularity, a growing number of users are composing extensive notes on these platforms. The analysis of these comments holds paramount importance for various business applications. Sentiment Analysis (SA), a technique within Natural Language Processing (NLP), plays a pivotal role in discerning the emotions conveyed in reviews and sentiments. This paper introduces the development of machine learning model using TF-IDFVectorizer (Term Frequency Inverse Document Vectorizer) Frequency for feature extraction technique. The aim is to predict the emotional activities of user comments through sentiment analysis. The primary objectives of this proposal are threefold. Initially, we collected a dataset and categorized the dataset for positive and negative. Secondly, we conducted a comprehensive comparison of seven classifiers such asDecision Tree, K Nearest Neighbor, Naïve Bayes, AdaBoost, XGBoost, Multi layer Perceptron, and Proposed Random Forest Model. Finally, we present the efficacy of our proposed model of Random Forest, showcasing state-of-the-art result in Borderland Emotion Dataset.

The selection of the most efficient features for glaucoma identification is the subject of our investigation because this disease is rapidly increasing worldwide. This disease causes lifelong blindness due to damage to the eye's optical nerve. Ophthalmologists have traditionally used tonometry, pachymetry, and other methods to measure intraocular pressure in order to diagnose patients. Yet each of these judgments takes time, requires high professional experience, and can be open to human error (inter-observer variability). Therefore, scholars are currently engaged in the domain of medical imaging, specifically focusing on the analysis of retinal images for the purpose of predicting glaucoma. This research also has the same objective and aims to address the aforementioned challenges. This empirical study proposes an artificial intelligence-based computer-assisted diagnosis (CAD) system which is built to overcome these difficulties by providing the best features for machine learning techniques for categorizing subject retinal pictures as "healthy" or "sick". This study presents a new set of reduced hybrid features that were selected from an initial set of 36 features extracted from fundus images of benchmark datasets that belonged to different classes to categorize patient fundus images into two categories: "healthy” or "infected." The nature inspired computing-based Emperor Penguin Optimization (EPO) algorithm and the Bacterial Foraging Optimization (BFO) algorithm are utilized to implement feature selection (FS) process. Additionally, a novel hybrid algorithm combining these two techniques is also proposed. Seven machine learning (ML) classifiers are engaged to compute eight statistically based performance metrics along with execution time computation, and a comparison of those metrics is also provided in a detailed fashion. The recommended method exhibits a fortunate performance with the highest specificity of 0.9940, sensitivity of 0.9347, and maximum accuracy of 96.55%. Expert medical practitioners who are overworked may receive assistance from the proposed system in making the optimal decisions to preserve human vision.

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.

The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace; 111 million instances of glaucoma are expected by 2040, up from 64 million in 2015. It causes widespread vision impairment. Optic nerve fibres can be degraded and cannot be replaced later in this disease. As a starting point, the retinal fundus images of glaucoma infected persons and healthy persons are used, and 36 features were retrieved from these images of public benchmark datasets and private dataset. Six ML models are trained for classification on the basis of the GSOA's returned subset of features. The suggested FS technique enhances classification performance with selection of most influential features. The eight statistical performance evaluating parameters along with execution time are calculated. The training and testing have been performed using a split approach (70:30), 5-fold cross validation (CV), as well as 10-fold CV. The suggested approach achieved 95.36 % accuracy. Due to its auspicious performance, doctors might use the suggested method to receive a second opinion, which would also help overburdened skilled medical practitioners and save patients from vision loss.

When contemplating the improvement of overall performance in machine learning (ML) models, a critical strategy for optimizing data preparation is feature selection (FS). There has been a significant rise in the popularity of metaheuristic FS algorithms in recent times. This can be attributed to their proficiency in accurately identifying and selecting the most relevant features for ML tasks. This study presents three feature selection strategies that utilize metaheuristic algorithms. The methodologies mentioned include the Gravitational Search Optimization Algorithm (GSA), Emperor Penguin Optimization (EPO), and a hybrid approach of GSA and EPO referred to as hGSAEPO. Previous research has explored the use of baseline algorithms for feature selection in various ML tasks. However, there is a lack of investigation regarding their application specifically in breast cancer(BC) classification. A combination of these two has been utilized for the first occasion. The purpose of selecting BC as the study of investigation is due to the reason that this illness is recognized as the second most prevalent cause of mortality in the female population. If the condition is detected in its initial phases, it can be remedied and can assist individuals in evading superfluous medical processes. The procedure of selecting relevant features holds significant importance in the purpose of predicting ailments like BC. The current research presents an innovative methodology that employs three soft-computing algorithms, EPO, GSA, and their proposed hybrid hGSAEPO to efficiently identify significant features while concurrently decreasing the occurrence of irrelevant ones, simplifying overall complexity and enhancing the accuracy. The utilization of these soft computing methodologies and six ML classifiers presents a viable framework for prognostic research through the classification of data instances on Wisconsin Diagnostic Breast Cancer (WDBC). The experimental findings of eight experiments conducted suggest that the suggested approach exhibits exceptional performance in the context of binary classification for BC by computing astounding results like precision of 0.9800, sensitivity of 0.9700, specificity of 0.9887, F1-score of 0.9539, area under the curve(AUC) surpassing 0.998, with an accuracy of 98.31%. We achieved our objectives by presenting a dependable clinical prediction system for healthcare professionals for efficient diagnosis.

In the current scenario machine learning is the branch of artificial intelligence being used in every field and medicine is one of them. In medical science, the use of machine learning techniques aims to improve patient care by collecting, and analyzing patient data, and designing advanced and intelligent tools and/or devices for disease detection using collective experience. ML technology detects patterns associated with specific diseases by analyzing large datasets that include various patient records, such as diabetes, blood pressure, cholesterol, X-rays, MRIs, CT scans, imaging data, and genomic information. ML algorithms compute the primary symptoms of the disease. Based on these calculations the disease is identified. Here it is necessary to have sufficient dataset and/or features for computation. The understanding of the ML model depends on the underlying feature to be used to identify the related problem. The fairness of a machine learning algorithm depends on which symptoms are selected to determine any disease. The selection of features for ML models is an important task, more or less features can make the model underfit or overfit. Incorrect determination of selected features can introduce bias into the model which can greatly affect the accuracy of the model. If the bias in the machine learning model is not properly tuned or the bias is tuned too high or too low then the prediction does not cover the underlined pattern. Diseases arise in different circumstances; each disease has its special characteristics. To cover all the basic parameters of each disease is a very tough task. If a basic attribute is missed and/or an attribute that has no relation to the disease is captured then the desired result of the model may be affected. In the proposed research paper, the feature selection problem and bias effect have been analyzed through the Support Vector Machine (SVM) and Logistic Regression (LR) algorithm.

Insufficient blood flow within the retinal vessels is believed to be the root cause of several optical problems, including partial vision loss and blindness. The appearance and growth of veins in retinal images are crucial for identifying ocular contamination. It is therefore a significant task for the research community to explore and segment these retinal blood vessels. For a wide range of purposes, including biometric authentication, computer-assisted laser surgery, automated screening, and the detection of ophthalmic pathologies like diabetic retinopathy (DR), age-related macular degeneration, and hypertensive retinopathy, among others, accurate blood vessel delineation in retinal images is used. Stroke and cardiovascular disease are severe illnesses that are preceded by changes in the properties of retinal blood vessels. There are many alternatives to identifying an infection a person has through their eyes in the broad development of innovation, such as diabetes, hypertension, heart disease, rheumatoid arthritis, etc. Examining retinal vascular features can therefore help to spot these changes and allow a person to respond quickly while the illness is still in its early stages. The use of computer-based automation in this procedure would reduce the costs associated with hiring qualified graders and solve the issue of inconsistency that results from manual grading. Numerous tasks involving retinal analysis require the segmentation of retinal vessels because it facilitates the execution of subsequent measures. As a result, segmentation is crucial to the study of retinal images. In the current study, a successful method for automatically extracting blood vessels from coloured retinal pictures is described. In order to solve the retinal blood vessel segmentation problem, we applied feature extraction methodologies and machine learning (ML) techniques in this research. This method uses pixel-based feature extraction, where each pixel has its own feature vector. Five distinct feature groups are employed for feature extraction: the basic line detector for line strength, the orthogonal line detector, the frangi filter, the average grey level features, and the gabor filter. The four machine learning classifiers—K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes, and Decision Tree—are trained using the training data. A DRIVE dataset is used to test the system; it is a benchmark, standard, widely accepted, and openly accessible to the general public. Blood vessel segmentation from fundus pictures is measured using performance metrics like precision, sensitivity, specificity, area under the curve (AUC), and accuracy as success indicators. With the given method, the maximum accuracy achieved is 0.9623 using KNN, 0.9463 using SVM, 0.9459 using Naive Bayes, and 0.9624 using Decision Tree. The findings of previously published extremely efficient approaches are comparable to the performance of the suggested strategy, which has demonstrated great performance. The recommended method can be used as an alternate automated tool for segmenting retinal blood vessels.

In the case of communicable diseases, such as COVID-19, effective and quick testing techniques make it easier to identify a contaminated person, so that he or she can be easily isolated. To predict COVID-19-infected individuals through chest computed tomography scans, this study suggests an effective feature selection technique incorporated in clinical decision support system that may be used for testing. After pre-processing, we retrieved 213 features from the chest computed tomography images of a public data set with 2482 images. Then, in a two-step process, the most significant features for recognizing the difference between COVID patients and healthy individuals are selected. Initially, the Chi-square test selects 75

The SARS-CoV-2 coronavirus strain’s introduction in December 2019 resulted in the development of the new coronavirus disease, COVID-19. Following its first appearance, the virus quickly spread throughout the world and is now considered to be a pandemic. There were 6,885,962 recorded deaths and 689,853,908 confirmed cases as of April 4, 2023. It is essential to put in place a thorough testing strategy in order to stop the disease from spreading. Nonetheless, a number of testing approaches are presently under consideration due to a restricted inventory and a limited supply of testing equipment. Recent expert analysis suggests that images from chest computed tomography (CT) scans could reveal important information about COVID-19, which is why we are using this modality as a focus of our investigation. Moreover, numerous recent studies have demonstrated that selecting the most informative features from the subject images improves the classification models’ efficiency and shortens the time needed for training and testing. All of this encourages us to present a study that suggests a novel, efficient, and fast feature selection system as the core of the proposed highly competent clinical decision support system for COVID-19 infection prediction. Using a publicly accessible CT image dataset, this four-phase system divides the images into two categories: "COVID-19-infected human" and "healthy human". Pre-processing is the first step in the study, after which features from different categories in the images are extracted in the next phase. In the third phase, the most influential features are then selected using three algorithms: the Teaching Learning-Based Optimization Algorithm (TLBO), the Cuckoo Search Optimization Algorithm (CSO), and a proposed hybrid of these two. To the best of the authors’ knowledge, these algorithms have rarely been applied to feature selection for COVID-19 infection prediction, which highlights the originality and inventiveness of the work. Following that, five machine learning (ML) classifiers that made use of the features selected during the feature selection stage are used to categorize the chest CT scans. Multiple tests, implementing the 70:30 approach, are then conducted for thorough investigation, and several performance-measuring metrics were computed during each test. A thorough investigation has been performed through multiple tests, and during each test, several performance-measuring metrics have been computed. Such in-depth investigations have rarely been published in state-of-the-art studies. With the suggested methodology, a noteworthy categorization accuracy of 97.94

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the total number of characteristics used. We evaluated the efficacy of the proposed approach in classifying eye-related glaucoma illnesses. In this study, we aim to assist professionals in identifying glaucoma by utilizing a proposed clinical decision support system that integrates image processing, soft-computing algorithms, and machine learning, and validates it on benchmark fundus images. Initially, we extract 65 features from the 646 retinal fundus images in the ORIGA benchmark dataset, from which a subset of features is created. For two-class classification, different machine learning classifiers receive the elected features. Employing 5-fold and 10-fold stratified cross-validation has enhanced the generalized performance of the proposed model. We assess performance using several well-established statistical criteria. The tests show that the suggested computer-aided diagnosis (CAD) model has an F1-score of 97.50

One of the essential data pre-processing methods for enhancing the performance of machine learning (ML) models is feature selection. Because they choose the most optimal features for ML problems, metaheuristic feature selection algorithms have gained popularity recently. The Gravitational Search Optimization Algorithm (GSOA), Emperor Penguin Optimization (EPO), and an integrated (hGSEPO) algorithm that combines GSOA and EPO are three metaheuristic feature selection algorithms that are presented in this paper. GSOA performs the global search in hGSEPO, and Emperor Penguin Optimizer (EPO) performs a more thorough local search. In order to find influential features while eliminating irrelevant features and reducing complexity, this article introduces a pioneering hybrid approach that combines the two distinct algorithms GSOA and EPO. While the baseline algorithms have been employed for feature selection in a few ML tasks, the hybrid of these two has been used for the first time for breast cancer (BC) classification. The reason for selecting BC as a case of investigation is due to its recognition as the second leading cause of death in women. According to earlier research, the feature selection (FS) stage is crucial when processing large datasets with the goal of forecasting medical conditions like BC. Based on the selection of the most important features necessary to achieve enhanced accuracy, this intelligent classification system divides the data from the benchmark BC Wisconsin Diagnostic Breast Cancer (WDBC) feature set into two classes. Additionally, the intention of the research is to ascertain the minimum quantity of features necessary to attain a higher level of accuracy. The experimental results show that the proposed approach works auspiciously and categorizes with astounding results, with the highest accuracy of 97.66%, 0.9687 sensitivity, 1.000 specificity, 1.000 precision, 0.9516 F1-score, and 0.9980 area under the curve (AUC).

Glaucoma, commonly known as the silent thief of sight, is the second most common cause of blindness in humans, and the number of cases is steadily increasing. Conventional diagnostic methods utilized by ophthalmologists include the assessment of intraocular pressure using tonometry, pachymetry, etc. Yet, each of these evaluations is time-consuming, requires human involvement, and is prone to subjective errors. In order to overcome these hurdles, practitioners are studying retinal pictures for glaucoma diagnosis within the field of medical imaging. In addition, computer-assisted diagnosis (CAD) systems can be created to solve these obstacles by using machine learning approaches to classify retinal pictures as "healthy" or "infected." This work presents a reduced set of structural and nonstructural features(characteristics) to characterize pictures of the retinal fundus. The grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRM), the first order statistical matrix (FOS), the wavelet, and the structural features (like disc damage likelihood scale (DDLS) and cup to disc ratio (CDR)) are extracted. This set of features is sent to three classical soft computing algorithms (Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Binary Cuckoo Search (BCS)) and their two-layered model (PSO-ABC) to generate subset of reduced features (feature selection phase) that computes auspicious accuracy when sent to three machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and Ensemble of RF, SVM, and Logistic Regression). According to our understanding, these four soft computing algorithms are rarely employed in this application field. For analyzing the performance of suggested strategy, the ORIGA, REFUGE, and their combinations are chosen as subject datasets. Standard statistical performance indicators, including accuracy, specificity, precision, and sensitivity, are calculated. The BCS delivers remarkable performance with a minimum of 91% accuracy and a maximum of 98.46% accuracy. PSO-ABC heavily decreases the original feature set, with minor accuracy sacrifices. The quantitative results are also compared in light of the most recent state-of-the-art published research. Owing to its exemplary performance, the suggested method will undoubtedly serve as a second opinion for ophthalmologists.

Feature selection is one of the crucial data preprocessing techniques for improving the performance of machine learning (ML) models. Recently, metaheuristic feature selection algorithms have become popular because they select optimal features for ML problems. This paper presents three feature selection strategies based on metaheuristic algorithms: Bacterial Foraging (BFOA), Emperor Penguin (EPO), and a hybrid (hBFEPO) combining BFOA and EPO. The baseline algorithms have been investigated for feature selection in other ML tasks, but not for breast cancer classification. A hybrid of these two has been used for the first time. These strategies were initially tested on the COVID-19 dataset. After achieving satisfactory results, these strategies are evaluated on the WDBC Breast Cancer dataset. The performance of our models on WDBC is compared with recent eighteen state-of-the-art studies. The results indicate that the hBFEPO model outperforms other models, achieving 100% precision and specificity, 98.49% accuracy, 95.43% sensitivity, a 95.99% F1-score, and a 99.60% AUC.

Aging and demographic changes have increased disease, multimorbidity, and disability. Price increases, rising demand, and social pressure on healthcare providers are further developments. Inefficiency lowers productivity. Remember that it was fiscal conservatism that drove the implementation of unnecessary austerity measures, which is why the healthcare system is struggling and financing for similar systems is limited. The universal healthcare coverage (UHC) that must be achieved by 2030 will require considerable system modifications. Machine learning, the most prevalent form of AI, may let us do more with fewer resources, which could affect our lifestyle. The impact of digital technology on healthcare systems has proved hard to predict. This chapter discusses how AI could be used to improve the responsiveness, equity, and efficiency of healthcare systems in order to attain UHC. Artificial intelligence is used to research optimality. AI in ophthalmology is used to treat common disorders such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of infancy, age-related or congenital cataracts, and retinal vein occlusion.

Feature selection, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data, is one of the most important aspects of the machine learning area. A major part of big data preprocessing is feature selection (reduction). There are 2n alternative feature subsets for every n features, making it difficult to choose the best set of features from a dataset using typical feature selection techniques. Consequently, the present study proposes and suggests a unique feature selection method based on the Eagle Strategy(ESO) Optimization, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. We chose this infection as our subject of investigation since the number of women with breast cancer is increasing rapidly on a global scale. After lung cancer, which affects more women than any other kind of cancer, breast cancer is the second leading cause of cancer mortality. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set (Wisconsin Diagnostic Breast Cancer (WDBC)) and to choose the fewest features (feature selection) to achieve maximum accuracy. This work also provides a hybrid technique for finding important features that combines two algorithms, ESO and the GSO algorithm, while reducing insignificant characteristics (features) and complexity. Soft computing technologies and machine learning algorithms provide a framework for prognostic research by classifying data instances as relevant or irrelevant depending on cancer severity. Thus, this work presented a new approach for classifying breast cancer tumors. In this research, we coupled soft computing methodologies—our implemented algorithms are applied for the first time to this problem—with artificial intelligence-based machine learning strategies to create a prediction model. The efficacy of our suggested technique was evaluated using WDBC breast cancer data sets, and the findings show that our proposed hybrid algorithm performs very well in breast cancer classification. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9705, specificity up to 1.000, precision up to 1.000, F1-score up to 0.9696, and an AUC up to 0.9980 (close to maximum, i.e., 1.0000). Our study's goal is to incorporate our findings into a valid clinical prediction system, allowing visual science specialists to make more accurate and effective judgments in the future. Furthermore, our suggested technology might be used to detect a wide range of diseases.

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2 n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight.

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional data. The FS approach selects influential traits and ignores the rest. This approach improves machine learning (ML) classifiers by reducing computational complexity and solution time. This empirical study presents a novel and effective methodology that uses two contemporary state-of-the-art soft-computing algorithms, the Grey Wolf Optimizer (GWO) and the Whale Optimization Algorithm (WOA). We have also created the hybrid version (hGWWO) of these two approaches as our novel, innovative scientific contribution. The baseline algorithms above have been used previously for feature selection across different domains. According to our understanding, these three algorithms are being used for the first time in glaucoma identification, particularly on the publicly available benchmark dataset, ORIGA. The rising global prevalence of glaucoma prompted this proposed methodology's focus on the illness. This illness is second only to cataracts in causing visual loss. Medical imaging professionals are examining retinal scans to diagnose glaucoma. Manual eye screening and retinal fundus imaging for confirmation of this infection require skilled ophthalmologists. The screening analysis method is time-consuming, requires experienced staff, and is subject to observational differences. In order to overcome these issues and to support the medical fraternity, an artificial intelligence-supported computer-aided clinical decision support system (CA-CDSS) is implemented in the present endeavor for confirmation of this disease from retinal fundus images. Nature-inspired computing strategies for feature selection and ML models for classification are employed to classify fundus retinal images under investigation. From the ORIGA dataset, sixty-five features were retrieved. A subset of most influential features is selected from the original dataset using three soft-computing-based FS methods. ML classifiers are trained using this portion of data and evaluated using a 70:30 technique. The suggested method yielded 96.8% accuracy, 0.981 specificity, 0.992 sensitivity, 0.969 precision, and a 0.982 F1-score. This study shows fresh initiatives with positive effects on ophthalmologists, researchers, and the public.

Glaucoma is the second most common cause of vision loss. Manual screening of a patient's eye or screening through a fundus image of the patient's eye requires expert ophthalmologists. This screening analysis is time-consuming, requires expert human involvement, and is subject to human intra-observer variability. Thus, medical imaging professionals are working to solve these issues by investigating retinal images for glaucoma detection using artificial intelligence-based computer-aided diagnosis systems (CAD). Machine learning algorithms (for classification) and nature-inspired computing (for feature selection/reduction) embedded CAD systems can successfully identify retinal pictures and can be employed to overcome these challenges. This proposed work is a productive attempt in which we have proposed two novel two-layered approaches (BA-BCS, BCS-PSO) which are based on Particle Swarm Optimization (PSO), Binary Cuckoo Search (BCS), and Bat Algorithm (BA). We have also analyzed the performances of BA, BCS, and PSO separately. These five (single and two-layered) approaches are used to compose subsets of reduced features that can generate the maximum accuracy when forwarded to three machine learning classifiers. Benchmark publicly available datasets, ORIGA and REFUGE, and their combinations are used to validate the proposed methodology. A maximum accuracy of up to 98.95% is achieved using these approaches. Apart from this, many other trade-off solutions are also suggested for the researcher's community. This study therefore presents novel efforts with new and efficient results that are beneficial to ophthalmologists, researchers, and humanity.