Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

  • R. L. Priya Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India
  • S. Vinila Jinny Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India
Keywords: Machine Learning, Chronic diseases, Data mining, Internet of Things (IoT), Deep Learning

Abstract

     World statistics declare that aging has direct correlations with more and more health problems with comorbid conditions. As healthcare communities evolve with a massive amount of data at a faster pace, it is essential to predict, assist, and prevent diseases at the right time, especially for elders. Similarly, many researchers have discussed that elders suffer extensively due to chronic health conditions.  This work was performed to review literature studies on prediction systems for various chronic illnesses of elderly people. Most of the reviewed papers proposed machine learning prediction models combined with, or without, other related intelligence techniques for chronic disease detection of elderly patients at an early stage to avoid emergency situations. This method provides a promising approach in the analysis of either structured or unstructured datasets to produce very substantial pattern discoveries. By defining the generic architecture for the prediction model, we reviewed various papers involved in similar fields, based on suggested methodologies and their associated outcomes. The study discussed the pros and cons of different prediction models using traditional and modern machine learning techniques.

Author Biography

S. Vinila Jinny, Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India

 

 

Published
2021-09-30
How to Cite
Priya, R. L., & Jinny, S. V. (2021). Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review. Iraqi Journal of Science, 62(9), 3138-3151. https://doi.org/10.24996/ijs.2021.62.9.29
Section
Computer Science