Leveraging Support Vector Machines For Early Disease Detection Using Electronic Health Records Dr. S. Uma1*

Abstract

Early disease detection using electronic health records (EHRs) is vital for improving patient outcomes and reducing healthcare costs. This proposed methodology integrates Support Vector Machines (SVM) for predictive modeling, emphasizing data preprocessing, feature selection, and SVM model design. Key steps include data collection, preprocessing, feature selection, and SVM model training with various kernel functions. The decision function of the SVM is described, and the SVM with kernel algorithm is outlined. This approach aims to enhance early disease detection capabilities, leveraging EHR data effectively.