A Novel Artificial Spider Monkey Based Random Forest Hybrid Framework for Monitoring and Predictive Diagnoses of Patients Healthcare.

Early diagnosis of diseases such as cancer, cardiovascular, diabetes, HIV, AIDS, Lyme, and tuberculosis can enable timely treatment, which enhances efficacy and helps reduce the severity and mortality while lowering overall healthcare costs. Predictive analytics is an emerging approach that comprehends trends in patient data to identify patterns in patient data, detect subtle signs of diseases, and enable early diagnosis for more effective treatment. However, traditional predictive diagnosis approaches rely on statistical models that are contingent on the data's average pattern and are unable to capture the data's inherent patterns, leading to ineffective and inaccurate diagnoses. In this paper, we introduce a novel Artificial Spider Monkey-based Random Forest (ASM-RF) hybrid framework that combines the predictive analytics of a Random Forest algorithm with Artificial Intelligence to evaluate patient health data, spot patterns, diagnose modest indications, and automate intelligent decisions for enhancing the healthcare system. The proposed ASM-RF hybrid framework employs a fitness function to evaluate the spider monkey's performance at the classification layer and update accuracy and recall, resulting in more accurate patient disease diagnoses and automating timely treatment decisions for improving the overall healthcare system. Moreover, we use Identity-based Encryption (IBE), which enables the encryption of data with private and public keys coupled with users' identities as the encryption keys, assisting in enhancing the security of the healthcare system. Adataset collected from three different IoT sensor devices, ten participants, and twelve activities is employed to simulate the proposed ASM-RF hybrid framework, which is then contrasted to cutting-edge predictive diagnostic algorithms in terms of accuracy, precision, Area under the Curve, execution time, recall, and F-measure. The proposed method exhibits superior performance when compared to conventional methods, as evidenced by its exceptional accuracy (99.52%), precision (99.12%), Area Under the Curve (AUC) (99.00%), recall (99.22%), F-measure (97.12%), and significantly reduced execution time (6s).