Key result
The proposed IoT-based machine learning framework achieved a classification accuracy of 92.31% for heart attack detection and 97.5% for ECG heartbeat multi-class classification using the K-Nearest Neighbors algorithm.
Why the study?
Many diseases may be preventable if predicted from patient data, and real-time remote clinical monitoring via IoT can support early diagnosis of treatable conditions.
Answers are grounded in this paper and cite their sources.
An IoT-based machine learning framework utilizing KNN and Gridsearch optimization achieved high accuracy in classifying ECG heartbeats and detecting heart attacks.
A lot of diseases may be preventable if they can be analyzed or predicted from patient historical and family data. Predicting diagnosis depends on the gathered clinical and physiological data of patients. The more collected clinical and medical healthcare data, the more knowledge the medical support system may support. Hence, real monitoring clinical and healthcare data for patients is the trend of this decade based on Internet of Things technologies (IoT). IoT models facilitate human life by easily collecting clinical data remotely for recognizing diseases that are easily treatable if it is diagnosed early. This paper proposes a framework consisting of two models: (i) heart attack detection model (HADM); (ii) Electrocardiosignal ECG heartbeat multiclass-classification model (ECG-HMCM). Gridsearch is used to the hyperparameters optimization for different machine learning (ML) techniques. The used dataset in HADM consists of 1190 patients and 14 features. As the foundation of diagnosing cardiovascular disease is arrhythmia detection hence, we propose an ECG heartbeat multi-class classification model using MIT-BIH Arrhythmia and PTB Diagnostic ECG signals dataset which contains five categories with 109446 samples. K Nearest Neighbor (KNN) technique is applied to build ECG-HMCM in addition to the using of Gridsearch algorithm for hyperparameter optimization aiming to improve the accuracy of classification which achieved 97.5%. The proposed framework aims to facilitate human life by easily collecting clinical data remotely. The outcomes of the experiments show that the suggested framework works well in a practical setting.
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Alnaggar et al. (2023) studied Heart conditions (Heart attack and Arrhythmia) (n=1,190). IoT-based Framework using Machine Learning (KNN, SVM, RF) vs. Other machine learning models was evaluated on Classification accuracy. The proposed IoT-based machine learning framework achieved a classification accuracy of 92.31% for heart attack detection and 97.5% for ECG heartbeat multi-class classification using the K-Nearest Neighbors algorithm.
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