A Classification Approach for Heart Disease Diagnosis using Machine Learning
J. R. V. Jeny, Nalla Sreeja Reddy, P Aishwarya, Samreen
Abstract
Heart Disease (HD)is a critical condition that many people suffer from and it is the expression which is used to characterize different types of heart conditions. Many people suffer with heart issues Heart disease remains the number one killer disease, especially in the United States. The main symptoms of heart disease are smoking, obesity, alcohol, high blood cholesterol level, hypertension etc. There are many algorithms that are used to improve the performance in health care system through information gathering in the form of datasets and it is used for various applications that are advanced in healthcare industry. In our paper, we used four machine learning classification techniques. Firstly, we included Support Vector Classifier (SVC). Is used to predict the model based on parameters considered. second one is the Logistic regression(LR). It is used to describe the classification problems based on the input parameters. Thirdly, Naive Bayes (NB) classifier and finally, the Decision tree (DT) Algorithm. We used different classifiers to boom the accuracy and overall performance. The principle intension of our proposal is to improve the accuracy rate and to identify heart disease by implementing the machine learning classifier techniques.