MACHINE LEARNING-DRIVEN DIAGNOSTIC MODELS FOR CARDIOVASCULAR DISEASE FORECASTING

Heart is the most crucial & critical organ of the human body. Life is completely dependent on the efficient working & functioning of our heart. It is one of the major causes of mortality in today's world. Heart disease remains one of the most serious health issues of our day. It is said to be the primary motive in death globally. Many times it's difficult for medical professionals to expect a heart disease on time. Nowadays, the health sector contains a lot of precious hidden facts & information which could prove to be very helpful in making predictive decisions especially in the field of medicine. Data mining is a method or technique used to analyze vast datasets and then derive significant and useful results with the use of extraordinary AI-based techniques. This article attempts to use three of these AI-based methods namely Decision Tree, Naïve Bayes, & Neural Network for forecasting cardiovascular or heart disease. All of these methods will be evaluated based on different unique & parameters with optimizations for better accuracy. The accuracy of each method will then be compared depending on accuracy based on various parameters. The best & accurate technique is then implemented for predicting whether or not a man or a woman will have coronary heart disease. This technique can be used by medical practitioners for early prediction of the disease so that timely care can be taken by the patient.