Prediction and Diagnosis of Heart Disease Patients Using Data Mining Techniques

Abstract

We live in a postmodern period, and our daily routines are undergoing significant changes that have a beneficial and bad influence on our health. As a consequence of these developments, the prevalence of numerous illnesses has skyrocketed. Heart disease, in particular, has grown increasingly prevalent in recent years. Human life is at risk. Blood pressure, sugar, pulse rate differences and so on may generate heart diseases such as blockage or blood congestion. The result may be a cardiac failure, peripheral artery disease, cardiac episodes, stroke, and even a sudden heart attack. Various types of cardiac disease are detected and/or diagnosed by various medical tests taking a family medical history and other properties into consideration. However, predicting heart disease without any medical testing is very challenging. The goal of this initiative is to detect various cardiac problems and take all necessary actions to avoid them at a reasonable charge as early as feasible. For the prediction of cardiac disorders, we use the ‘Data mining' methodology, in which characteristics are input into SVM, Random forest, KNN, and ANN classification algorithms. Preliminary readings and studies acquired with this technology are used to determine the likelihood of discovering cardiac illnesses at an early stage, when they may be entirely treated with the correct diagnosis.