Empowering Home Health Care: Precision Unleashed with an Innovative Hybrid Machine Learning Approach for Tailored Patient Classifications

Abstract

Governments are actively seeking solutions to address the growing issue of longer waiting times for patients. To reduce the strain on the public sector and its increasing workload, the governmental bodies have established collaborative agreements with private healthcare service providers. While the private sector is expanding, it is not growing rapidly enough to meet the rising demands for healthcare services. Consequently, there is a dire need to explore innovative management techniques aimed at reducing patient wait times, cutting costs, and enhancing the quality of healthcare. In this paper, we propose an innovative solution to tackle the patient classification problem (PCP) using the machine learning paradigm. The proposed approach involves a hybridization of two classifiers, one utilizing the aggregation method and the other employing the support vector machine technique. We compare classification algorithms, including KNN, SVM, SVM + AM, and logistic regression, and evaluate their performance in terms of precision, recall, specificity, F1-score, and overall accuracy. The SVM + AM is found to be the best model for the classification of patients, followed by SVM, KNN, and logistic regression. We believe that such an evaluation will help addressing the challenges associated with patient classification, the medical practitioners, and, in turn, contribute to the overall healthcare system.