Utilizing Classification Algorithms to Predict Heart Disease

Cardiovascular disease is one of the leading deadly diseases in worldwide and also the ranks among the primary global causes of mortality, posing a significant challenge for medical practitioners in forecasting heart attacks due to the intricate nature of the task, demanding substantial expertise and insight. Presently, the healthcare domain accommodates sensitive data that, while not integral to decision-making, requires careful handling. Building a classification model for heart disease prediction involves several steps, including data preprocessing, model selection, training, and evaluation. To address this, classification algorithm like Random forest, Decision Tree(DT), KNN were deployed to anticipate heart attacks in this investigation. The findings of this research exhibit a remarkable precision in prognostication. Through data mining, the healthcare sector gains the ability to discern underlying trends within databases.