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Predict the Chances of Heart Abnormality in Diabetic Patients Through Machine Learning

Monika Saraswat*, A. K. Wadhwani, Sulochana Wadhwani

Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, 474005, India

* Corresponding Author: Monika Saraswat. Email: email

Journal on Artificial Intelligence 2022, 4(2), 61-76.


Today, more families are affected by Diabetes Mellitus (DM) disease on account of its continually increasing occurrence. Most patients remain unknown about their health quality or the DM’s risk factors prior to diagnosis. The medical world has witnessed that individuals are affected by two different diabetes namely a) Type-1 diabetes (T1D), as well as b) Type-2 diabetes (T2D). As Type 2 Diabetes affects the other organs of the body, the proposed system concentrates specifically on Type 2 Diabetes. This work aims to ascertain the cardiac disorder in T2D patients. As of the ECG dataset, the requisite data is gathered it contains healthy volunteer and patients record with pathologies like Myocardial Infarction, Cardiomyopathy, Bundle branch block, Dysrhythmia, from the dataset, the system regarded 245 persons of data in which 160 volunteers are non-diabetic and 85 volunteers are diabetic. The classification is performed. Here, a K-Nearest Neighbor (KNN), Multi-layer Perceptron’s (MLP), along with Support Vector Machines (SVM) learning models is concerned for the investigation of typical cardiac abnormality in diabetic persons. From the attained outcomes, the proposed work could be perceived to show maximal accuracy and minimal error rate percentage in the least time while comparing existing machine learning algorithms. KNN attained 80%, MLP attained 93.8% and SVM attained 96.25% of accuracy, respectively.


Cite This Article

M. Saraswat, A. K. Wadhwani and S. Wadhwani, "Predict the chances of heart abnormality in diabetic patients through machine learning," Journal on Artificial Intelligence, vol. 4, no.2, pp. 61–76, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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