Key result
A machine learning model using five ranked features extracted from single-lead ECG signals achieved an accuracy of 87.19% in classifying diabetic and non-diabetic subjects.
Why the study?
Diabetes causes cardiac autonomic neuropathy which affects ECG signal patterns, prompting the development of automated feature extraction methods to classify diabetic versus normal ECG signals.
Does feature extraction and machine learning classification of single-lead ECG signals accurately identify patients with diabetes?
Observational (n=86)
No
Does feature extraction and machine learning classification of single-lead ECG signals accurately identify patients with diabetes?
Machine learning classification using five ranked features from single-lead ECG signals can identify type-2 diabetes with 87.19% accuracy.
Answers are grounded in this paper and cite their sources.
May aid noninvasive diabetes screening; leaves open prospective validation before clinical adoption.
Diabetes Mellitus is a chronic medical condition in which the body is unable to properly regulate the amount of glucose (a type of sugar) in the blood. It can cause serious consequences like heart disease, nerve damage, and kidney illness. Diabetes causes cardiac autonomic neuropathy, which affects the pattern of electrocardiogram (ECG) signals. ECG measures electrical activity of the hearts. In this paper, the features extraction method is proposed for the classification of diabetic ECG and normal ECG signals. Ten features, namely, log energy, threshold, Shannon, sure entropy, root mean square value, kurtosis, skewness, maximum value, energy, and variance are extracted from the single-lead ECG signal. Fisher-score has been employed for features ranking methods the ranked features are used as input to the classifiers namely medium tree, coarse Tree, linear discriminant, quadratic discriminant, and Gaussian naive Bayes, classifiers. The five ranked features using medium tree classifier has produced an accuracy of 87.19%. The analysis of performance measurement shows the effectiveness of the proposed method in the classification of diabetic and non-diabetic ECG signals.
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Jain et al. (2023) conducted an observational in Diabetes Mellitus (n=86). Medium tree classifier using five ranked ECG features vs. Healthy controls was evaluated on Classification accuracy of diabetic vs normal ECG signals. A machine learning model using five ranked features extracted from single-lead ECG signals achieved an accuracy of 87.19% in classifying diabetic and non-diabetic subjects.
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