title: Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases using Machine Learning Techniques.
abstract: OBJECTIVE
  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.
authors.affiliation: College of Nursing, Keimyung University, Daegu, Korea.
authors.forename: Soo-Kyoung
authors.initials: SK
authors.lastname: Lee
authors.affiliation: Department of Nursing, Soonchunhyang University, Cheonan, Korea.
authors.forename: Youn-Jung
authors.initials: YJ
authors.lastname: Son
authors.affiliation: College of Nursing, Seoul National University, Seoul, Korea.
authors.forename: Jeongeun
authors.initials: J
authors.lastname: Kim
authors.affiliation: Biomedical Knowledge Engineering Lab., Seoul National University, Seoul, Korea.
authors.forename: Hong-Gee
authors.initials: HG
authors.lastname: Kim
authors.affiliation: School of Dentistry, Seoul National University, Seoul, Korea.
authors.forename: Jae-Il
authors.initials: JI
authors.lastname: Lee
authors.affiliation: School of Mechanical Engineering, Kyungpook National University, Daegu, Korea.
authors.forename: Bo-Yeong
authors.initials: BY
authors.lastname: Kang
authors.affiliation: Electronics and Telecommunications Research Institute, Daejeon, Korea.
authors.forename: Hyeon-Sung
authors.initials: HS
authors.lastname: Cho
authors.affiliation: Biomedical Knowledge Engineering Lab., Seoul National University, Seoul, Korea.
authors.forename: Sungin
authors.initials: S
authors.lastname: Lee
keywords: Aged; Artificial Intelligence; Chronic Disease; Quality of Life