ItemConference Paper

IOT Wearable Medical Device for Heart Disease Recognition Based ML and DL: A Classification Approach

Title
IOT Wearable Medical Device for Heart Disease Recognition Based ML and DL: A Classification Approach
Creator
Ajesh, F.; Philip, Felix M.; Jims, Anupama; Alapatt, Bosco Paul
Description
In the past few years, heart disease has become the foremost worldwide contributor to mortality. This ailment, with a profound effect on the functioning of the heart, leads to issues such as infections in the coronary arteries and diminished blood vessel performance. These complications can culminate in severe unlikely events like heart attacks and strokes. In India alone, approximately one person succumbs to heart disease every minute. To curb the fatalities stemming from cardiac disorders, there is an urgent need for a swift and efficient detection strategy. IoT sensors are utilized in conjunction with Machine Learning (ML) and Deep Learning (DL) techniques to identify heart disease. In this research, we have successfully applied IoT devices and a sensor network to detect heart diseases. This study introduces a medical IoT device designed to gather heart data from patients both before and after the onset of heart disease. This continuously transmitted data is processed using RBF, MLP, and Bi-LSTM models for predicting heart disease. The deep learning approach utilizes past analyses to learn critical features related to heart disease, achieving efficiency in handling complex data. After conducting a series of experiments, we evaluate the systems performance using metrics such as f-measure, sensitivity, specificity, loss function, and Receiver Operating Characteristic (ROC) curves. The HDRBi-LSTM method, in combination with IoT-based analysis, achieves an impressive accuracy rate of 99.5% with minimal time complexity (5 s), effectively reducing heart disease mortality by simplifying the diagnosis of this condition. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Source
Lecture Notes in Networks and Systems;Volume;1243 LNNS;pp.353-363
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
deep learning; Heart Disease prediction; IoT; long short-term memory; Supervised learning
Coverage
Ajesh F., Department of Computer Science and Engineering, Sree Buddha College of Engineering, Kerala, Alappuzha, India; Philip F.M., Department of CS & IT Jain (Deemed-to-be University), Bangalore, India; Jims A., Department of Computer Science and Engineering, CVV Institute of Science and Technology, Kerala, India; Alapatt B.P., CHRIST (Deemed to be University), Delhi-NCR, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-303181079-4;
Format
online
Language
English
Type
Conference paper

Citation

Ajesh, F.; Philip, Felix M.; Jims, Anupama; Alapatt, Bosco Paul, “IOT Wearable Medical Device for Heart Disease Recognition Based ML and DL: A Classification Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/25317.

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