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Improving Early Prognosis of Dementia Using Machine Learning Methods

Authors:

Georgios Katsimpras,

Maria-Esther Vidal,

Georgios PaliourasAuthors Info & Claims

ACM Transactions on Computing for Healthcare (HEALTH), Volume 3, Issue 3

Article No.: 30, Pages 1 - 16

https://doi.org/10.1145/3502433

Published: 07 April 2022 Publication History

Abstract

Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.

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Cited By

Addae SKim JSmith ARajana PKang M(2024)Smart Solutions for Detecting, Predicting, Monitoring, and Managing Dementia in the Elderly: A SurveyIEEE Access10.1109/ACCESS.2024.342196612(100026-100056)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3421966
Ho SChien TLin MTsai K(2023)An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft ExcelMedicine10.1097/MD.0000000000032670102:4(e32670)Online publication date: 27-Jan-2023
https://doi.org/10.1097/MD.0000000000032670
Vyas AAisopos FVidal MGarrard PPaliouras G(2022)Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical recordsBMC Medical Informatics and Decision Making10.1186/s12911-022-02004-322:1Online publication date: 17-Oct-2022
https://doi.org/10.1186/s12911-022-02004-3

Index Terms

Improving Early Prognosis of Dementia Using Machine Learning Methods
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning

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Information

Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 3, Issue 3

July 2022

251 pages

EISSN:2637-8051

DOI:10.1145/3514183

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2022

Accepted: 01 November 2021

Revised: 01 November 2021

Received: 01 May 2021

Published in HEALTH Volume 3, Issue 3

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Cited By

Addae SKim JSmith ARajana PKang M(2024)Smart Solutions for Detecting, Predicting, Monitoring, and Managing Dementia in the Elderly: A SurveyIEEE Access10.1109/ACCESS.2024.342196612(100026-100056)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3421966
Ho SChien TLin MTsai K(2023)An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft ExcelMedicine10.1097/MD.0000000000032670102:4(e32670)Online publication date: 27-Jan-2023
https://doi.org/10.1097/MD.0000000000032670
Vyas AAisopos FVidal MGarrard PPaliouras G(2022)Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical recordsBMC Medical Informatics and Decision Making10.1186/s12911-022-02004-322:1Online publication date: 17-Oct-2022
https://doi.org/10.1186/s12911-022-02004-3

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