A Study on Machine Learning and Supervised and Deep Learning Algorithms to Predict the Risk of Patients

Abstract

Technological innovation is adopted sequentially in the medical sciences and health sciences. Due to the innovation of devices, methods, researches, the medical science sector is developing rapidly and its impact helps health professionals to identify diseases easily, predict patients' future diseases probabilities, etc. Using the Framingham dataset, a model built where the ML classification algorithm Linear regression, Logistic regression, SVC, Decision tree, Random Forest, Naive Bayes algorithms, have been used to predict the possibilities of a patient’s next ten-year coronary heart disease risk. DL model Artificial neural networks and the robust ML algorithm impact learning are also used in this research to find the best model and comparison between ML and DL models. After accessing all the ML models, the Logistic regression was found the best effective one with an accuracy score of 0.85063. The Artificial Neural networks and the impact learning provide an accuracy score of 0.84061 and 0.84971 respectively. The aim is to find out the best model which can be easily adopted.