Cardiovascular Disease Prediction Using Machine Learning Classifiers

cardiovascular illnesses are believed to be the major cause of death on a global scale. According to WHO data, cardiac illnesses are thought to be the cause of 30% of fatalities in developing nations. One in in four persons will get heart disease, especially as they get older. Machine learning classifiers have been used to predict the risk of cardiovascular diseases by analyzing a variety of clinical, demographic, and lifestyle data. By training on large, diverse datasets, these classifiers can identify patterns and relationships between different variables and accurately predict CVD risk in individuals. In the proposed study we have utilized the kaggle and IEEE data port datasets for estimating the chances of heart disease. A model was developed using seven machine learning classifiers such as Random Forest, Logistic Regression, CART, KNN, Naive Bayes, Support vector machine (SVM), and Ada Boost. A variety of evaluation metrics can be used to evaluate the performance of the model. We have included accuracy, precision, sensitivity, specificity, as well as ROC, F1 score, Mathew correlation and log loss. The accuracies of the model when treated with each classifier were 94%, 78%, 91%, 77%, 75%, 76%, and 78% respectively. The data visualization was created to show how the features relate to one another. The results of the studies show that the random forest method validates 2213 data instances using 12 attributes with 94% accuracy.