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Ensemble based Machine Learning Approach for Patient Health Monitoring

by Maruf Rahman, Joydeep Roy, Tanuja Das
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 24
Year of Publication: 2024
Authors: Maruf Rahman, Joydeep Roy, Tanuja Das
10.5120/ijca2024923698

Maruf Rahman, Joydeep Roy, Tanuja Das . Ensemble based Machine Learning Approach for Patient Health Monitoring. International Journal of Computer Applications. 186, 24 ( Jun 2024), 1-9. DOI=10.5120/ijca2024923698

@article{ 10.5120/ijca2024923698,
author = { Maruf Rahman, Joydeep Roy, Tanuja Das },
title = { Ensemble based Machine Learning Approach for Patient Health Monitoring },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2024 },
volume = { 186 },
number = { 24 },
month = { Jun },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number24/ensemble-based-machine-learning-approach-for-patient-health-monitoring/ },
doi = { 10.5120/ijca2024923698 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-06-27T00:56:26.952534+05:30
%A Maruf Rahman
%A Joydeep Roy
%A Tanuja Das
%T Ensemble based Machine Learning Approach for Patient Health Monitoring
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 24
%P 1-9
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the present era, healthcare analysis has been continuously evolving to serve the medical demands of the patients. Due to the advancement in the sensor technology, to be used in the wearable health devices for monitoring the health status of the people, enormous quantity of data is being produced progressively. Wearable healthcare equipment is essential for early detection and treatment of chronic diseases. An in-depth study is required to design a healthcare system that collects, records, analyses and share large data streams containing medical information of the users in real-time and efficiently to increase health risks and reduce health-related costs. Many machine learning based approaches have been prominent in the classification of sensors data from these healthcare devices. In this work, an ensemble based classifier has been designed based on majority voting consisting of five types of classifiers namely, logistic regression, XGB classifier, support vector machines, AdaBoost classifier and artificial neural networks from which the results of the physiological sensor data are predicted from the clinical environment. The models are evaluated using four evaluation metrics that are confusion matrix, accuracy, precision and recall.

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Index Terms

Computer Science
Information Sciences
Machine Learning
Health monitoring

Keywords

Vital signs sensor data logistic regression XGB classifier support vector machines AdaBoost classifier artificial neural networks ensemble learning

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