A Hybrid Cardiovascular Disease Diagnosis and Prediction System Using Machine Learning Approach


Shakeel Juman TP
Faculty of Computer Science and Application, Centre for Computer Science and Information Technology (CCSIT), Kerala, India.


The biggest cause of deaths worldwide is the cardiovascular disease and nowadays, its prediction at an early stage is of great importance. In this paper, Cardiovascular disease prediction is done by adopting Supervised Learning Algorithms using the patient’s medical record and the comparison of results are done with the known supervised classifiers Decision tree, Random forest, Logical regression,K nearest neighbor classifier and Naïve bayes. Classifier is used to classify the patient record information. To determine the risk of cardiovascular disease,attributes are given as input to the classifier in the classification stage 14. The Physicians can diagnose the disease in a more effective way using this proposed system. The records collected from 303 patients is used to test the efficiency of the classifier. The results shows that the prediction of the likelihood of cardiovascular patients can be done using various classifiers in the most efficient way.