research-article

Automatic Prediction of Depression in Older Age

Authors:

Peter A. BathAuthors Info & Claims

ICMHI '19: Proceedings of the 3rd International Conference on Medical and Health Informatics

Pages 36 - 44

https://doi.org/10.1145/3340037.3340042

Published: 17 May 2019 Publication History

Abstract

Maintaining good mental health such as the prevention of severe depressive symptoms is critical for physical health and well-being in older adulthood. However, depression in elderlies is not known quite well and thus cannot be treated adequately. In this study, a large and wide variety of influencing factors from multiple domain areas were investigated using a large nationally representative sample of older people from the English Longitudinal Study of Ageing (ELSA). Five different machine learning algorithms were employed to build the models for the prediction of depression in older age. Several model ensemble strategies were proposed to merge the results from individual predictive models in order to further improve prediction performance. Significant risk or protective factors associated with depressive symptoms in the elder were separately identified in each domain area. The findings from this study will enhance our understanding about the underlying pathophysiology of depression, thus helping develop appropriate intervention strategies to prevent or reduce the onset of depression in older age.

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Cited By

Selvaraj AMohandoss L(2024)Enhancing Depression Detection: A Stacked Ensemble Model with Feature Selection and RF Feature Importance Analysis Using NHANES DataApplied Sciences10.3390/app1416736614:16(7366)Online publication date: 21-Aug-2024
https://doi.org/10.3390/app14167366
Razavi MZiyadidegan SMahmoudzadeh AKazeminasab SBaharlouei EJanfaza VJahromi RSasangohar F(2024)Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping ReviewJMIR Mental Health10.2196/5371411(e53714)Online publication date: 21-Aug-2024
https://doi.org/10.2196/53714
Islam MHassan SAkter SJibon FSahidullah M(2024)A comprehensive review of predictive analytics models for mental illness using machine learning algorithmsHealthcare Analytics10.1016/j.health.2024.1003506(100350)Online publication date: Dec-2024
https://doi.org/10.1016/j.health.2024.100350
Show More Cited By

Index Terms

Automatic Prediction of Depression in Older Age
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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ICMHI '19: Proceedings of the 3rd International Conference on Medical and Health Informatics

May 2019

207 pages

ISBN:9781450371995

DOI:10.1145/3340037

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

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Publication History

Published: 17 May 2019

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ICMHI 2019: The third International Conference on Medical and Health Informatics 2019

May 17 - 19, 2019

Xiamen, China

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Cited By

Selvaraj AMohandoss L(2024)Enhancing Depression Detection: A Stacked Ensemble Model with Feature Selection and RF Feature Importance Analysis Using NHANES DataApplied Sciences10.3390/app1416736614:16(7366)Online publication date: 21-Aug-2024
https://doi.org/10.3390/app14167366
Razavi MZiyadidegan SMahmoudzadeh AKazeminasab SBaharlouei EJanfaza VJahromi RSasangohar F(2024)Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping ReviewJMIR Mental Health10.2196/5371411(e53714)Online publication date: 21-Aug-2024
https://doi.org/10.2196/53714
Islam MHassan SAkter SJibon FSahidullah M(2024)A comprehensive review of predictive analytics models for mental illness using machine learning algorithmsHealthcare Analytics10.1016/j.health.2024.1003506(100350)Online publication date: Dec-2024
https://doi.org/10.1016/j.health.2024.100350
Yong SHung MYuan CChiu CHuang MYou C(2023)Mind and Body: The Complex Role of Social Resources in Understanding and Managing Depression in Older AdultsProceedings of the ACM on Human-Computer Interaction10.1145/35795077:CSCW1(1-25)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579507
Lakshmi Prasanna Mehrotra S(2023)Comparative Analysis of Machine Learning Algorithms on Mental Health DatasetICT with Intelligent Applications10.1007/978-981-99-3758-5_54(599-606)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-981-99-3758-5_54
Makhdoomi SRakhra MSingh DSingh A(2022)Artificial-Intelligence based Prediction of Post-Traumatic Stress Disorder (PTSD) using EEG reports2022 5th International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I56241.2022.10072671(1073-1077)Online publication date: 14-Dec-2022
https://doi.org/10.1109/IC3I56241.2022.10072671
Tao XChi ODelaney PLi LHuang J(2021)Detecting depression using an ensemble classifier based on Quality of Life scalesBrain Informatics10.1186/s40708-021-00125-58:1Online publication date: 15-Feb-2021
https://doi.org/10.1186/s40708-021-00125-5
Mahendran NDurai Raj Vincent PSrinivasan KSharma VJayakody D(2020)Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in AdultsIEEE Access10.1109/ACCESS.2020.29778878(49509-49522)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2977887

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