Objectives: Malnutrition remains the leading cause of child mortality in Tanzania, with over 34% of children under 5 years of age affected by stunting and approximately 5% experiencing acute malnutrition. This study aimed to develop a machine learning model to predict fetal nutritional status using maternal and clinical data, thereby enabling early risk identification for health workers and parents and facilitating timely intervention. To enhance practical applicability, the model was deployed within a mobile application to provide accessible, real-time predictions that support prompt clinical and behavioral responses.Methods: Using a dataset collected in Tanzania, the performance of multiple binary classification algorithms—logistic regression, multi-layer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM)—was compared using the geometric mean and F-measure. These models were trained on clinical data from 11,703 pregnant women to predict fetal nutritional status based on maternal and clinical variables.Results: The results indicated that the LightGBM algorithm achieved the best overall performance in predicting fetal nutritional status. The most influential predictors included maternal age, weight, fetal age, hemoglobin level, number of meals per day, medical history, and education level. Additionally, 93% of respondents reported satisfaction with the application’s predictive functionality, supporting its potential utility for early intervention in low-resource settings.Conclusions: These findings highlight the potential of data-driven approaches to address public health challenges in maternal and child health. The proposed model may enable healthcare providers to make timely, informed decisions that improve maternal and fetal outcomes, ultimately contributing to the reduction of child malnutrition in Tanzania.
Objectives: This study aimed to apply machine learning (ML) techniques to predict postoperative length of stay (LOS) in patients with hip fracture. Because LOS varies widely across individuals owing to complex clinical factors, accurate prediction remains challenging. To address this challenge, an enhanced two-stage approach was developed and compared with a conventional one-stage approach.Methods: Data from 3,118 surgically treated patients with hip fracture were extracted from a hospital information system. Demographic and perioperative variables were analyzed, and the dataset was divided into training and test sets at a 70:30 ratio. ML algorithms were applied using a two-stage modeling approach. In the first stage, a classification model categorized LOS as short stay or long stay. In the second stage, regression models predicted the number of hospital days within each group. Performance was evaluated using accuracy, precision, recall, and F1-score for classification and mean absolute error (MAE), root mean square error (RMSE), and mean relative error (MRE) for regression.Results: In the one-stage approach, the support vector machine model showed the lowest prediction error, with an MAE of 2.20, RMSE of 3.18, and MRE of 0.42. The two-stage approach, which integrated classification and regression, outperformed the onestage approach, achieving an MAE of 1.46, RMSE of 1.86, and MRE of 0.35.Conclusions: The two-stage approach outperformed the one-stage approach, suggesting that LOS stratification improves prediction accuracy. This improvement may help hospitals anticipate resource needs, plan postoperative care, and manage bed allocation more effectively.
Objectives: Parkinson’s disease (PD) is a chronic neurodegenerative disorder characterized by central nervous system dysfunction. Early identification may enable prompt treatment and help slow the progression of disabling symptoms. Previous studies have reported that clinical assessments based on visual interpretation may be insufficiently accurate and often miss early PD. Therefore, this study aimed to develop an automated single-photon emission computed tomography-based model for binary classification of healthy controls and patients with early PD.Methods: We analyzed 514 DaTSCAN images from the Parkinson’s Progression Markers Initiative database, using one unique scan per individual. The workflow comprised three main stages: image processing; computation of 23 features, including radial, threshold, boundary, and striatal binding ratio features; and image classification using machine learning algorithms.Results: The medium Gaussian support vector machine achieved an accuracy of 97.09% ± 1.53% (95% confidence interval, 95.11%–99.07%), sensitivity of 98.18% ± 1.48%, specificity of 93.85% ± 3.44%, and area under the receiver operating characteristic curve of 98.49% ± 1.31%. This performance was significantly higher than that of the three-dimensional convolutional neural network baseline model (accuracy, 94.55% ± 2.98%; p = 0.045), while requiring substantially less training time.Conclusions: In this dataset, carefully designed feature engineering combined with classical machine learning outperformed the deep-learning baseline when the training data were limited and the selected features aligned with clinical diagnostic criteria. This approach achieved high accuracy for early PD detection and may provide computational efficiency and interpretability suitable for clinical implementation.
Objectives: Inherited retinal dystrophies are characterized by high genetic and phenotypic heterogeneity, and their clinical progression may alternate between latent and active phases. Identifying the onset of the active phase may support earlier intervention for inflammatory retinal degeneration. Plasma proteomics has shown potential for characterizing predictive biomarkers in retinal diseases, but its application remains experimental. This study aimed to develop a methodological simulation exercise to evaluate the performance of machine learning (ML) models in distinguishing active and latent phases using an artificially generated dataset.Methods: An artificial dataset of 500 samples was created, and plasma proteomic profiles were generated for each sample using arbitrary values. Sample classification was based on a pathway activation score. Four ML models were tested: support vector machine, random forest, logistic regression, and extreme gradient boosting. Each model was trained across a range of hyperparameters.Results: Logistic regression achieved the best performance, with an accuracy of 0.73, precision of 0.73, and F1-score of 0.73.Conclusions: This simulation study showed that synthetic proteomic datasets can be used to evaluate ML approaches for distinguishing active and latent phases of retinal dystrophies when real data are scarce. Synthetic data can support the creation of targeted datasets for proteins associated with retinal dystrophies, helping to address the limited availability of suitable open-source data.
