Classification of Chronic Kidney Disease based on health care records using machine learning with Support Vector Machine

Abstract

Chronic Kidney Disease (CKD) is a global health concern with a rising prevalence that necessitates early and accurate diagnosis for effective management. This study proposes the application of Machine Learning (ML), specifically Support Vector Machine (SVM), to classify CKD based on health care records. Leveraging a comprehensive dataset of patient health records, including clinical and demographic information, the research aims to develop a predictive model that can assist in the timely identification of individuals at risk of CKD. The methodology involves preprocessing the health care records, extracting relevant features, and implementing the SVM algorithm for classification. The dataset is divided into training and testing sets to evaluate the model's performance.  The SVM classification model that was developed after going through the data preprocessing process produced results that were good enough to be able to classify whether a patient was diagnosed with CKD or not with an accuracy level of 98% and a total of 400 lines of data and 25 features.