Machine Learning Algorithms for Heart Disease Prediction: A Comparative Analysis

Machine learning has become popular today as so many of its algorithms are now commonly used in different data science projects in various industries especially in the health care sector. It is imperative for researchers and medical professionals to be assisted by machine learning methods in early detection of diseases such as heart disease which is one major killer of humans in our world today. Health and life threat can be prevented with a correct prediction of heart diseases, and an incorrect prediction of this disease can prove to be fatal. Machine learning algorithms are excellent at learning from data, and since healthcare providers generate huge amount of data on a daily basis, these algorithms can thrive in this field. In this research study, a comparative analytical approach was taken in the determination of which algorithm performs better under the given condition. Various experiments were carried out using cross validation of 5 and 10 folds, to ensure that models created can generalize well enough. This study makes use of data from University of California, Irvine (UCI) machine learning database containing 303 instances with 14 attributes. The collected data is scaled using Min-Max normalization technique. Different popular models are built using supervised machine learning classification algorithms on the scaled data such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and Gradient Boosting ensemble method. These algorithms are also evaluated using standard performance metrics such as precision, recall, and F1-score. From the experiments carried out, it can be concluded that SVM performs better as it out performs the other algorithms.