Cardiovascular Disease Prediction Using Machine Learning and XAI

Heart illness may be a critical wellbeing condition that includes an eminent effect on worldwide mortality, particularly among more seasoned people. Per WHO information, a stunning 17. 9 million people pass absent each year due to heart illness, showing a concerning increment within the number of cases. Distinguishing issues ahead of time through the application of machine learning strategies such as K-Nearest Neighbor (KNN), Random Forest, Decision Tree,Logistic Regression, Support Vector Machine, and Naive Bayes has the potential to upgrade determining accuracy and decrease the event of human botches. This think about evaluates different machine learning calculations on datasets that incorporate traits like age, sex, sort of chest torment, blood weight, and glucose levels. Through the utilization of Logistic Regression, a forecast precision of 83. 90% was accomplished. These improvements have the potential to progress the early location and treatment viability, eventually profiting pending healthcare strategies.