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COPD Severity Prediction in Elderly with ML Techniques

Authors:

Sotirios Alexiou,

Konstantinos MoustakasAuthors Info & Claims

PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments

Pages 185 - 189

https://doi.org/10.1145/3529190.3534748

Published: 11 July 2022 Publication History

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a disease characterized by persistent symptoms mainly in the respiratory system and permanent restriction of airflow. It can worsen over time and develop into a serious illness, being one of the leading causes of morbidity and mortality worldwide. In the context of this study, we focus on the early prediction of the COPD patients’ severity grades, especially those over 55 years of age. For this purpose, we employ Machine Learning (ML) techniques in order to design appropriate models that will efficiently estimate the severity level based on the most crucial risk factors for disease development. These models will be embedded in the AI Framework of the GATEKEEPER system, which aims to provide personalized risk assessment and interventions to the elderly population.

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Digital Library

Cited By

Dritsas ETrigka M(2024)Utilizing Multi-Class Classification Methods for Automated Sleep Disorder PredictionInformation10.3390/info1508042615:8(426)Online publication date: 23-Jul-2024
https://doi.org/10.3390/info15080426
Tripathi GSabherwal MSingh P(2024)“I know I have this till my Last Breath”: Unmasking the Gaps in Chronic Obstructive Pulmonary Disease (COPD) Care in IndiaProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642504(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642504
Makkena KNatarajan K(2023)Classification Algorithms for Liver Epidemic IdentificationEAI Endorsed Transactions on Pervasive Health and Technology10.4108/eetpht.9.43799Online publication date: 13-Nov-2023
https://doi.org/10.4108/eetpht.9.4379
Show More Cited By

Index Terms

COPD Severity Prediction in Elderly with ML Techniques
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Other conferences

PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments

June 2022

704 pages

ISBN:9781450396318

DOI:10.1145/3529190

Conference Chair:
Fillia Makedon

Copyright © 2022 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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Association for Computing Machinery

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Publication History

Published: 11 July 2022

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Funding Sources

This work has been supported by the European Union?s H2020 research and innovation programme GATEKEEPER under grant agreement No 857223, SC1-FA-DTS-2018-2020 Smart living homes-whole interventions demonstrator for people at health and social risks.

Conference

PETRA '22

PETRA '22: The15th International Conference on PErvasive Technologies Related to Assistive Environments

June 29 - July 1, 2022

Corfu, Greece

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Cited By

Dritsas ETrigka M(2024)Utilizing Multi-Class Classification Methods for Automated Sleep Disorder PredictionInformation10.3390/info1508042615:8(426)Online publication date: 23-Jul-2024
https://doi.org/10.3390/info15080426
Tripathi GSabherwal MSingh P(2024)“I know I have this till my Last Breath”: Unmasking the Gaps in Chronic Obstructive Pulmonary Disease (COPD) Care in IndiaProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642504(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642504
Makkena KNatarajan K(2023)Classification Algorithms for Liver Epidemic IdentificationEAI Endorsed Transactions on Pervasive Health and Technology10.4108/eetpht.9.43799Online publication date: 13-Nov-2023
https://doi.org/10.4108/eetpht.9.4379
Trigka MDritsas E(2023)Long-Term Coronary Artery Disease Risk Prediction with Machine Learning ModelsSensors10.3390/s2303119323:3(1193)Online publication date: 20-Jan-2023
https://doi.org/10.3390/s23031193
Xu X(2023)Predicting the Risk of Chronic Kidney Disease Using Machine LearningSSRN Electronic Journal10.2139/ssrn.4636627Online publication date: 2023
https://doi.org/10.2139/ssrn.4636627
Trigka MDritsas EMylonas P(2023)A Multi-Class Classification Approach for Anemia Level Prediction with Machine Learning Models2023 8th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)10.1109/SEEDA-CECNSM61561.2023.10470847(1-6)Online publication date: 10-Nov-2023
https://doi.org/10.1109/SEEDA-CECNSM61561.2023.10470847
Dritsas ETrigka MMylonas P(2023)Ensemble Machine Learning Models for Breast Cancer IdentificationArtificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops10.1007/978-3-031-34171-7_24(303-311)Online publication date: 2-Jun-2023
https://doi.org/10.1007/978-3-031-34171-7_24
Dritsas ETrigka M(2022)Machine Learning Techniques for Chronic Kidney Disease Risk PredictionBig Data and Cognitive Computing10.3390/bdcc60300986:3(98)Online publication date: 14-Sep-2022
https://doi.org/10.3390/bdcc6030098
Dritsas ETrigka MMylonas P(2022)A Multi-class Classification Approach for Weather Forecasting with Machine Learning Techniques2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)10.1109/SMAP56125.2022.9942121(1-5)Online publication date: 3-Nov-2022
https://doi.org/10.1109/SMAP56125.2022.9942121
Dritsas EAlexiou SMoustakas K(2022)Efficient Data-driven Machine Learning Models for Hypertension Risk Prediction2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)10.1109/INISTA55318.2022.9894186(1-6)Online publication date: 8-Aug-2022
https://doi.org/10.1109/INISTA55318.2022.9894186

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