Predictive Analysis Of Cardiovascular Disease Using Random Forest Algorithm

This research gives a comprehensive analysis of cardiovascular disease data obtained from Kaggle, to predict the progression and commencement of cardiovascular diseases (CVDs). CVDs, a primary global health concern, covers a range of conditions affecting the heart and blood vessels, causing significant risks to public health. This study investigates the impact of various factors that influence CVDs such as age, gender, resting blood pressure, serum cholesterol, fasting blood sugar, maximum heart rate, exercise-induced angina, and the number of major blood vessels affected. Using exploratory data analysis techniques, the research seeks to expose the patterns and relationships among these variables. Using Machine Learning techniques such as Random Forest Algorithm, the risk factors of CVDs have been evaluated. In the end, the study proves to be a promising impact on public health and raise awareness about the heart health.