Diagnosis of Heart Disease Using Machine Learning Methods

The World Health Organization (WHO) estimated 12 million deaths around the world appear each year from heart disease. Heart disease includes coronary artery disease, heart rhythm problem and heart defects. Each disease has similar symptoms but cause different effects and severity on patient. The common factors of heart disease include high blood pressure, diabetes, cholesterol and age. These factors are independent of each other; thus, the use of artificial intelligence and machine learning will be a suitable choice to model them. Correct diagnosis of heart disease is difficult due to the complicated processes and different system and it is vital because heart disease can lead to a heart attack, chest pain, stroke and sudden death. Hence, an accurate and early detection of heart disease with proper and adequate treatment is needed. The main aim of this research is to identify suitable feature selection method and machine learning algorithms for the diagnosis of heart disease. Chi Square Feature Selection (CSFS), Random Forest Feature Selection (RFFS), Forward Feature Selection (FFS), Backward Feature Selection (BFS) and Exhaustive Feature Selection (EFS) are the feature selection methods applied in this research. These feature selection methods are then implemented in the machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k-Nearest Neighbour (KNN). The performance of these machine learning algorithms is evaluated through accuracy, sensitivity and specificity based on Confusion Matrix, ROC Curve and area under ROC (AUC). Based on the results, combination of RF with RFFS produced the highest accuracy value with 85.25% accuracy.