ArticleHealthcare informatics research2014
Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases using Machine Learning Techniques.
Article in Healthcare informatics research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Opportunities and challenges in pooling health-related quality-of-life data for prediction modeling in breast cancer across Europe: lessons from the EORTC BALANCE project.ESMO real world data and digital oncology · 2025Article
- Article
- Machine-learning model predicting quality of life using multifaceted lifestyles in middle-aged South Korean adults: a cross-sectional study.BMC public health · 2024Article
- Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms.BMC medical informatics and decision making · 2023Article
- Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?Biomedical engineering online · 2023Article
- A Prediction Model for Osteoporosis Risk Using a Machine-Learning Approach and Its Validation in a Large Cohort.Journal of Korean medical science · 2023Article
- Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods.Frontiers in nutrition · 2023Article
- The development of machine learning in bariatric surgery.Frontiers in surgery · 2023Review
- Clinical decision support system for quality of life among the elderly: an approach using artificial neural network.BMC medical informatics and decision making · 2022Article
- Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization.Journal of King Saud University. Computer and information sciences · 2022Article
- Identification and Prediction of Chronic Diseases Using Machine Learning Approach.Journal of healthcare engineering · 2022Article
- Functional Status and Health-Related Quality of Life in Patients with Peripheral Artery Disease: A Cross-Sectional Study.International journal of environmental research and public health · 2021Article
- Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine.Computers in biology and medicine · 2021Article
- Self-Attention-Guided Recurrent Neural Network and Motion Perception for Intelligent Prediction of Chronic Diseases.Journal of healthcare engineering · 2021Article
- Application of Machine Learning Methods in Nursing Home Research.International journal of environmental research and public health · 2020Article
- Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study.Psychiatry investigation · 2020Article
- Artificial intelligence, machine learning, and deep learning in women's health nursing.Korean journal of women health nursing · 2020Article
- Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network: A Study Based on the Scandinavian Obesity Surgery Registry.Journal of clinical medicine · 2019Article
- Modifiable Lifestyle Factors and Cognitive Function in Older People: A Cross-Sectional Observational Study.Frontiers in neurology · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe 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.
methodsWe 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.
resultsFIVE 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.
conclusionsOur 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.
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