Review: Heart Diseases Detection by Machine Learning Classification Algorithms

Most human deaths are caused by heart diseases. Such diseases cannot be efficiently detected for the lack of specialized knowledge and experience. Data science is important in healthcare sector for the role it plays in bulk data processing. Machine learning (ML) also plays a significant part in disease prediction and decision-making in medical care industry. This study reviews and evaluates the ML approaches applied in heart disease detection. The primary goal is to find mathematically effective ML algorithm to predict heart diseases more accurately. Various ML approaches including Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), t-Distributed Stochastic Neighbor Embedding (t-SNE), Nave Bayes, and Random Forest were utilized to process heart disease dataset and extract the unknown patterns of heart disease detection. An analysis was conducted on their performance to examine the effecacy and efficiency. The results show that Random Forest out-performed other ML algorithms with an accuracy of 97%.