Cardiovascular Disease Prediction using Boosting Algorithms

Heart and blood vessel illnesses are collectively referred to as cardiovascular diseases. Smoking, having high blood pressure, having high cholesterol, and being obese may all raise the chance of developing certain cardiovascular illnesses. Heart disease diagnosis is a challenging undertaking that can offer an automated assessment of the patient's heart health to enhance the efficiency of future treatment. Therefore, machine learning algorithms are required to make better decisions in the prediction of heart diseases. Boosting algorithms combine multiple weak learners to a strong learner in a sequentially manner which iteratively improves the observations. The prediction of cardiovascular disease is important since it allows for the possibility of saving lives. To enable comparisons, the results are presented separately. Based on the findings, it was concluded that the proposed model generated the maximum accuracy with XG Boost, 90%.