Feature Extraction and Machine Learning Models for Heart Disease Prediction and Classification

Now adays, heart disease is one of the major problems in human life. This disease alone is responsible for dying ofmore people per year in comparison to all the other diseases integrated. It includes various problems related to the heart like Blood vessel disease (coronary artery disease), Born heart problems (congenital heart defects), Irregular heartbeats(arrhythmias) and many more. Also, this disease is very commonly seen in this new generation especially the youths. Study says youths are suffering with heart diseases due to chronic stress, smoking, poor lifestyle, etc. So, the growing cases of heart disease makes us feel that we should have some advanced algorithms and works regarding heart disease prediction. This work intends to find whether the patient or the person is suffering or likely to suffer from heart disease or not. This research paper aims to implement supervised machine learning like Decision trees (DT), Random Forest (RF), K-nearest neighbors, Gaussian Naï ve Bayes algorithms, Logistic Regression and SVC. The dataset used in this work has been collected from Kaggle and the accuracy by Decision Tree and Random Forest are 98.33%.