Prediction of Cardiovascular Health Status Using Machine Learning Algorithms

Machine Learning is one of the most extensively utilised Artificial Intelligence (AI) technologies in today's industrialized world. It is utilized in a variety of domains including trading, medicine, e-commerce, education, and many more. Prediction and classification are two domains where Machine Learning (ML) is used extensively. It is indispensable to go for a non manual heart disease prediction process so as to prevent dangers related with heart disease and to warn the infirm ahead of time about their peril of developing a cardio vascular problem. The best way to avoid the onset of such diseases is to diagnose them early, so that suitable drugs can be administered and the risk level may be decreased further. This research gives cardiac disease prediction using patient data. This technique predicts using 13 medical measures such as arterial blood pressure, cholesterin, and ECG findings. These metrics indicate the likelihood of a person developing cardiovascular disease by means of Artificial Intelligence techniques. To make good judgments and forecast the likelihood of cardiovascular disease in a person, several data mining techniques such as Logistic Regression, Decision Tree, Support Vector Machine, Naive Bayes, Random Forest, and K-Nearest Neighbor algorithms are utilised. The Jupyter Notebook platform is also used to examine the link between variables so as to forecast the likelihood of a person developing cardiovascular disease. The aforementioned methods are developed, and their accuracy is compared, with the algorithm with the greatest accuracy being utilised for this application.