Risk Assessment of Myocardial Infarction for Diabetics through Multi-Aspects Computing - Shankar Reddy, Shiva; Sethi, Nilambar; Rajender, R. (EAI Endorsed Transactions on Pervasive Health and Technology, 2020)
INTRODUCTION: Myocardial infarction (MI) is a type of cardiovascular disease. Cardiovascular disease is the major side effect of diabetes. It causes damage to heart muscle due to interruption in the blood flow. The chance of getting this disease is high in diabetes patients. OBJECTIVES: To choose a dataset with features related to diabetes, parameters of ECG and risk factors of MI for effective prediction. Predict myocardial infarction in both type-1 and type-2 diabetic patients using regression techniques. Recognise the best algorithm. METHODS: Multiple linear regression, ridge regression and lasso regression are existing techniques in addition to which proposed technique lasso regression is used to develop a model for prediction. The trained models are compared to know better performing algorithm. Estimation statistics namely confidence and prediction intervals are used to show the amount of uncertainty in predicted values. The statistical measures in regression analysis namely root mean squared error and r_squared value are used to evaluate and compare algorithms. RESULTS: The proposed algorithm ‘lasso regression’ has achieved better values of RMSE and r_squared as 0.418 and 0.2278 respectively compared to remaining techniques. CONCLUSION: Best performance of proposed algorithm was noticed and hence using lasso regression for prediction of myocardial infarction in diabetes patients gives better results.