An Effective Heart Disease Detection and Classification using Machine Learning

About 20.5 million people die yearly due to cardiovascular disease (CVD). Early prediction of heart disease and accurate heart severity identification can save the induvial life with timely medications. In many cases, most deaths occur due to inaccurate diagnosis of the heart. In computer science, various existing researchers have dealt with the classification of heart disease on synthetic as well as real-time datasets. But still, those systems have a challenge such as low classification accuracy, high error rate and inaccurate heart severity detection. After identifying all these challenges, we proposed an effective heart disease detection and classification using hybrid machine learning techniques. In this article, we describe how various feature extraction and hybrid machine learning classifiers produce accurate severity of heart disease on real-time datasets. In the first phase, we collected a Cleveland dataset from Kaggle and then applied preprocessing and normalization for data balancing. Various feature extraction and selection methods, such as TF-IDF, Co-relation coefficient, N-Gram, and bi-Gram features, are used for practical module training. The different machine learning we used on normalized datasets for classification. The five supervised machine learning algorithms are used, such as Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and Hybrid Machine Learning (HML) etc. The HML achieves 99.30% accuracy on the Cleveland dataset using Weka 3.8 machine learning framework. As a result, the proposed system compares with various heart disease predictions using machine learning techniques.