Support Vector Machines for Early Detection of Chronic Diseases in Healthcare

Abstract

Abstract— In order to enhance patient outcomes and ensure successful treatment, early diagnosis of chronic illnesses is crucial. The use of Support Vector Machines (SVM) to improve the precision and timeliness of chronic illness prediction is the focus of this study. By leveraging extensive healthcare datasets, including patient demographics, medical history, laboratory results, and clinical observations, SVM models are trained to identify early indicators of diseases such as diabetes, cardiovascular diseases, and cancer. Feature selection ensures the relevance of predictors, and cross-validation techniques validate the models' robustness. Comparative analysis reveals that SVM outperforms traditional diagnostic methods and other machine learning algorithms in sensitivity, specificity, and overall predictive accuracy. The study highlights the practical implications of integrating SVM models into clinical settings, addressing challenges like data integration, model interpretability, and the necessity for continuous updates, ultimately showcasing SVM's potential to revolutionize early detection and management of chronic diseases in healthcare..