Smart wearable model for predicting heart disease using machine learning

The heart diseases are one of the leading causes of death in today’s world. Wearable Technology is gaining a lot of attention in today’s world. This research focuses on developing a wearable biomedical prototype to predict the presence of heart disease. The research’s findings will be especially hel...

Full description

Saved in:
Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2022-09, Vol.13 (9), p.4321-4332
Main Authors: Jansi Rani, S. V, Chandran, K. R. Sarath, Ranganathan, Akshaya, Chandrasekharan, M, Janani, B, Deepsheka, G
Format: Article
Language:English
Subjects:
Algorithms
Cardiac arrhythmia
Cardiovascular disease
Chronic illnesses
Electrocardiography
Heart diseases
Heart rate
Internet of Things
Machine learning
Monitoring systems
Patients
Plant diseases
Prototype tests
Segments
Sensors
Support vector machines
Wearable technology
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The heart diseases are one of the leading causes of death in today’s world. Wearable Technology is gaining a lot of attention in today’s world. This research focuses on developing a wearable biomedical prototype to predict the presence of heart disease. The research’s findings will be especially helpful in countries where doctor to patient ratio is alarmingly low as wearable technology can be used to monitor parameters of patients anywhere, without restricting to hospital environments. The objective is to predict a possibility of heart disease using Machine Learning Algorithms. Electrocardiogram (ECG) patterns are obtained from the ECG sensor embedded in the wearable biomedical prototype. Variations in ECG patterns are monitored. ECG patterns are used to obtain the heart rate using the R-to-R method. Cleveland data set is used which has 13 attributes including ECG related attributes like resting ECG results, depression in ST-segment induced by exercise relative to rest and slope of peak exercise segment. The proposed system with the Random Forest Algorithm have predicted with an efficiency of 88%. For testing the prototype, human subjects are not involved rather we used static(real) data and the results are sent to the app to take necessary action. This prototype which is developed as a proof of concept will help the elderly people as an assistive equipment.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-022-03823-y