Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases using Machine Learning Techniques.
10.4258/hir.2014.20.2.125
- Author:
Soo Kyoung LEE
1
;
Youn Jung SON
;
Jeongeun KIM
;
Hong Gee KIM
;
Jae Il LEE
;
Bo Yeong KANG
;
Hyeon Sung CHO
;
Sungin LEE
Author Information
1. College of Nursing, Keimyung University, Daegu, Korea.
- Publication Type:Original Article
- Keywords:
Quality of Life;
Aged;
Chronic Disease;
Artificial Intelligence
- MeSH:
Aged*;
Chronic Disease*;
Complement System Proteins;
Decision Trees;
Humans;
Korea;
Logistic Models;
Machine Learning*;
Nutrition Surveys;
Quality of Life*;
Statistics as Topic;
Support Vector Machine
- From:Healthcare Informatics Research
2014;20(2):125-134
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: The purposes of this study were to identify the factors that affect the health-related quality of life (HRQoL) of the elderly with chronic diseases and to subsequently develop from such factors a prediction model to help identify HRQoL risk groups that require intervention. METHODS: We analyzed a set of secondary data regarding 716 individuals extracted from the Korea National Health and Nutrition Examination Survey from 2008 to 2010. The statistical package of SPSS and MATLAB were used for data analysis and development of the prediction model. The algorithms used in the study were the following: stepwise logistic regression (SLR) analysis and machine learning (ML) techniques, such as decision tree, random forest, and support vector machine methods. RESULTS: Five factors with statistical significance were identified for HRQoL in the elderly with chronic diseases: 'monthly income', 'diagnosis of chronic disease', 'depression', 'discomfort', and 'perceived health status.' The SLR analysis showed the best performance with accuracy = 0.93 and F-score = 0.49. The results of this study provide essential materials that will help formulate personalized health management strategies and develop interventions programs towards the improvement of the HRQoL for elderly people with chronic diseases. CONCLUSIONS: Our study is, to our best knowledge, the first attempt to identify the influencing factors and to apply prediction models for the HRQoL of the elderly with chronic diseases by using ML techniques as an alternative and complement to the traditional statistical approaches.
