Key result
The Support Vector Machine (SVM) algorithm achieved the highest accuracy of 96.25% in predicting cardiac abnormalities in diabetic patients, outperforming MLP and KNN.
Why the study?
Type 2 diabetes affects other body organs, and most patients are unaware of their health quality or risk factors prior to diagnosis, necessitating methods to ascertain cardiac disorders in T2D patients.
Do machine learning models accurately predict cardiac abnormalities from ECG data in diabetic patients?
Do machine learning models accurately predict cardiac abnormalities from ECG data in diabetic patients?
Absolute Event Rate: 96.25% vs 93.8%
Support Vector Machine (SVM) models can accurately classify cardiac abnormalities in diabetic patients using ECG data, achieving up to 96.25% accuracy.
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.
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Saraswat et al. (2022) studied Type 2 Diabetes Mellitus and Cardiac Abnormality (n=245). Support Vector Machine (SVM) vs. Multi-layer Perceptron (MLP) and K-Nearest Neighbor (KNN) was evaluated on Classification accuracy. The Support Vector Machine (SVM) algorithm achieved the highest accuracy of 96.25% in predicting cardiac abnormalities in diabetic patients, outperforming MLP and KNN.
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