Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection

Since cardiovascular diseases (CVDs) continue to rank among the leading causes of death worldwide, there is an urgent need for accurate and timely diagnostic tools. In this review, we investigate a machine learning-based system that uses optimum feature selection strategies to enhance CVD detection. The suggested method aims to improve diagnostic accuracy by concentrating on the most crucial patient characteristics, including age, gender, kind of chest pain, blood pressure, cholesterol levels, and other critical health markers. The Random Forest technique is used because it resists overfitting and performs well when dealing with high-dimensional data and nonlinear relationships. Results from experiments on a variety of datasets demonstrate that this model outperforms traditional diagnostic techniques in terms of prediction accuracy. This approach is a useful tool in clinical practice for lowering the risks and death rates related to cardiovascular illnesses since it may facilitate early- stage detection and prompt intervention.[2]