A comparative study of cardiovascular disease detection using machine learning techniques with an extended dataset

Heart disease, also referred to as cardiovascular disease (CVD), is a broad category of illnesses that obstruct the coronary arteries, impairing the cardiovascular system’s ability to operate normally. They are in charge of blood circulation, oxygen delivery, and the delivery of vital nutrients to various bodily areas. Heart disease claims the lives of over 17.9 million people worldwide each year, or about 31% of all deaths. The alarmingly high death toll from cardiovascular disease worldwide highlights the urgent need for thorough study and creative remedies. The current research uses various cutting-edge techniques in a multi-modal manner to address the detection of cardiovascular disease in response to this pressing need. A variety of machine learning models, such as random forests, decision trees, Naïve Bayes, k-nearest neighbors (KNN), and neural networks, were employed in conjunction with a voting ensemble technique to attain an accuracy of 89.2%. Therefore, in order to lessen the severe effects of cardiovascular disease, this study offers an improved strategy. Its adoption offers better individual health results and more effective control of the societal and financial challenges associated with cardiovascular disease.