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Smart wearable model for predicting heart disease using machine learning

Wearable to predict heart risk

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Abstract

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.

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Authors and Affiliations

  1. Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    S. V. Jansi Rani, K. R. Sarath Chandran, Akshaya Ranganathan, M. Chandrasekharan, B. Janani & G. Deepsheka

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  1. S. V. Jansi Rani
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  2. K. R. Sarath Chandran
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  3. Akshaya Ranganathan
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  4. M. Chandrasekharan
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  5. B. Janani
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  6. G. Deepsheka
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Correspondence to S. V. Jansi Rani.

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Jansi Rani, S.V., Chandran, K.R.S., Ranganathan, A. et al. Smart wearable model for predicting heart disease using machine learning. J Ambient Intell Human Comput 13, 4321–4332 (2022). https://doi.org/10.1007/s12652-022-03823-y

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  • DOI: https://doi.org/10.1007/s12652-022-03823-y

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