phat 20(24): e3

Research Article

Risk Assessment of Myocardial Infarction for Diabetics through Multi-Aspects Computing

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  • @ARTICLE{10.4108/eai.17-12-2020.167655,
        author={Shiva Shankar Reddy and Nilambar Sethi and R. Rajender},
        title={Risk Assessment of Myocardial Infarction for Diabetics through Multi-Aspects Computing},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={6},
        number={24},
        publisher={EAI},
        journal_a={PHAT},
        year={2020},
        month={12},
        keywords={Myocardial infarction, Diabetes, Multiple linear regression (MLR), Ridge regression (RR), Lasso regression, Confidence and prediction intervals, Root mean squared error (RMSE) and r_squared},
        doi={10.4108/eai.17-12-2020.167655}
    }
    
  • Shiva Shankar Reddy
    Nilambar Sethi
    R. Rajender
    Year: 2020
    Risk Assessment of Myocardial Infarction for Diabetics through Multi-Aspects Computing
    PHAT
    EAI
    DOI: 10.4108/eai.17-12-2020.167655
Shiva Shankar Reddy1,*, Nilambar Sethi2, R. Rajender3
  • 1: Research Scholar, Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, India
  • 2: Department of Computer Science and Engineering, GIET, Gunupur, Odisha, India
  • 3: Department of Computer Science and Engineering, LENDI Engineering College, Vizianagaram, India
*Contact email: shiva.shankar591@gmail.com

Abstract

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.