Machine Learning Driven Cardiovascular Disease Prediction Among Male Senior Adults

The heart is a critical organ at the heart of the circulatory system, acting as a powerful muscle pump that keeps life going by constantly pumping blood throughout the body. Heart illnesses, often known as cardiovascular diseases (CVDs), are a broad category of ailments that affect the heart and blood arteries. The severity of these illnesses ranges from minor to life-threatening. Heart disease prediction using machine learning is essential for improving patient care, reducing healthcare costs, and addressing a major public health challenge. It leverages advanced data analytics to enable early detection, personalized treatment, and more efficient allocation of resources, ultimately leading to better health outcomes and a positive impact on public health. Heart disease prediction involves the use of machine learning and data analysis techniques to assess an individual's risk of developing heart-related conditions. This process begins with collecting a wide range of medical data from kaggle for analysis, including demographics, medical history, and clinical measurements. In this analysis, Random forest is used as a feature estimate for the study of cardiovascular disease prediction for male senior adults in the age group of 37 to 70 years since we analyzed from the existing research that it performs better for heart disease. Recursive Feature Elimination with Cross-Validation (RFECV) is used to assess feature importance. It trains the model using logistic regression and achieves an accuracy of 82.4%. Predicting cardiovascular health among senior male adults is of utmost importance to proactively manage and mitigate the risks associated with these conditions.