Heart Disease Prediction: A Machine Learning Approach with Higher Accuracy

Abstract

Heart disease, a prevalent cardiovascular condition, poses significant health risks and affects millions worldwide. The alarming rise in heart disease cases in recent years demands proactive measures, making early prediction of these conditions crucial and concerning. By employing machine learning techniques, this study aims to identify patients who are more susceptible to heart disease based on diverse medical attributes. The Heart Disease Dataset from Kaggle, consisting of 1025 samples and 14 features, was incorporated into this investigation. And after preprocessing the dataset by removing duplicate and null values and implementing statistical imputation and several data graphs, like a scatter plot, box plot, histogram, etc., we split it into training and testing datasets and apply SMOTE technique on the training one. Various machinelearning approaches were used in this study, out of which the optimized decision tree gave the best accuracy of 98.96%.