Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset

Nowadays, one of the most important illnesses is a heart disease which causes most patients dead. The medical diagnosis of heart disease is quite difficult. This diagnosis is a challenging process that requires accuracy and efficiency. The chance of death will be decreased with early heart disease detection. Because cardiac problems are now a fairly frequent ailment, predicting heart disease has become one of the most difficult medical jobs in recent years. Researchers looked at a variety of closely related traits to discover the most reliable predictors of these conditions. In this study, Machine Learning (ML) techniques are used to identify the presence of cardiac abnormalities. The proposed method predicts the chances of heart disease and classifies patient's risk level by using different ML algorithm techniques such as Decision Tree (DT), Ada-Boost Classifier (AB), Extra trees Classifier (ET), Support vector Machine (SVM), Gradient boost, MLP, extreme gradient boost (XGB), Random Forest (RF), KNN, and LR. Three different datasets are combined to train and test the proposed system. The experimental results show that, when compared to other ML algorithms, the Decision Tree algorithm has the highest accuracy, at 99.16%.