Cardiac Disease Prognostication System

Cardiovascular (cardiac) disease is defined as a certain range of conditions and problems affecting the heart. At present, most health problems are due to heart disease. People may lead to death due to this disease by having some of the symptoms like angina (chest pain), hypertension, cholesterin, sugar, etc. Heart disease prediction is very much complicated. In this Data Science plays a significant act in the medical field. ML is a part of data science that produces a result with the correctness of the presence of heart disease for many patients. This can be very much helpful for medical researchers and doctors all over the world. Machine Learning is the sub-field of Data Mining. This paper uses Some Machine algorithms like Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Linear Regression, Naïve Bayes (NB), Logistic Regression, etc., in that the particular algorithm Random Forest has been given high accuracy and which can identify whether the cardiovascular disease is present or not for the patients. This can also be trained and validating the data for finding CVD. The dataset for this paper is collected from the UCI Repository it consists of four datasets for training and testing. This paper aims to create a web app for both doctors as well as for users (public).