Artificial Intelligence based Early Detection of Cardiovascular Diseases

Many sectors, including healthcare, education, agriculture, and industries, depend heavily on Machine Learning (ML) algorithms. Predicting cardiovascular disease is one of the world's biggest challenges. Many people may die as a result of coronary artery disease. Enormous research work is being carried out to identify the factors that could predict the development of cardiovascular disease. Heart disease prediction is currently being effectively resolved by utilizing techniques of Artificial Intelligence (AI) including machine learning algorithms and deep learning. This research study has implemented several AI-based classification algorithms such as Support Vector Machine, Random Forest, Logistic Regression, Decision Tree, and K-Nearest Neighbours (KNN) for the prediction of heart disease. Finally, this study employs performance indicators including the confusion matrix, accuracy score, F1-score, recall, precision, sensitivity, and specificity to analyze the model's effectiveness and performance. It is inferred from the experimental results that the highest classification accuracy of 91% is achieved for the Random Forest Classifier when compared to other machine learning algorithms on heart disease dataset.