Cardiovascular Disease Prediction based on Decision Tree

This study uses advanced data analytics approaches to explore the vital field of heart disease, a prevalent and potentially fatal disorder. The study uses supervised machine learning to identify predicted patterns in people's vulnerability to heart disease, primarily concentrating on the Decision Tree (DT) method. The study is taking place in the context of rising mortality rates from cardiovascular diseases, which highlights the critical need for preventative healthcare measures. Notably, the DT algorithm proves to be an exceptional performer, predicting the risk of cardiovascular illness with an astounding 99% accuracy. This supports its usefulness as a diagnostic tool and highlights how revolutionary preventative healthcare may be with its potential. The work adds to the growing conversation on the use of technology to solve pressing healthcare issues in addition to illuminating the predictive power of machine learning in cardiovascular health. The research's conclusions have ramifications for data-driven healthcare treatments and personalized therapy in the future, opening the door to creative ways to treat cardiac disease.