An IoT based Framework for Detecting Heart Conditions using Machine Learning

Abstract

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.