Heart Disease Detection using Machine Learning

Heart disease is one of the main causes of death worldwide, and early diagnosis is essential for successful treatment and the avoidance of unfavourable effects. With the use of massive datasets, sophisticated algorithms, and pattern recognition, machine learning has become an effective tool for identifying and diagnosing cardiac disease. Feature selection, dimensionality reduction, and ensemble learning are three machine learning approachesthat we integrate in this studyto provide a unique method for detecting heart disease. Our model outperforms current state-of-the-art techniques in terms of sensitivity and specificity, as well as high accuracy and resilience. Ourmethod is also very interpretable and offers information on the under lying causes of heart disease risk. These findings underscore the significance of current research in this crucial area and show how machine learning has the potential to increase the precision and effectiveness of heart disease identification