Heart Disease Prediction Using Machine Learning Algorithms

Heart disease is a prevalent and complex condition that affects numerous individuals worldwide. Timely and accurate diagnosis of heart disease is of utmost importance in cardiology. In this research article, we propose an efficient and precise system for heart disease diagnosis, employing machine learning techniques. The system is designed based on various classification algorithms, including Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest. Standard feature selection algorithms such as ANOVA, Chi-Squared, and Mutual Information Feature Selection (MIFS) are utilized to eliminate unrelated features. Furthermore, we introduce a novel fast experiment that contains a conditional mutual information feature selection algorithm, ANOVA feature selection algorithms, and a Chi-squared feature selection algorithm to address the feature selection challenge. These feature selection algorithms enhance classification model accuracy and reduce the compile time in the classification ML model. The cross-validation method evaluates the models and optimizes hyperparameters, ensuring reliable model assessment. Performance measuring metrics are utilized to assess the classifiers' performance. The classifiers are estimated based on the selected features determined by the feature selection algorithms. The experimental results show that the ANOVA F-test feature selection algorithm along with the Support Vector Machine classifier, is a viable approach for developing an advanced intelligent system that can identify heart disease. The proposed model can also be easily implemented in healthcare to facilitate heart disease identification.