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Clinical decision support system for quality of life among the elderly: an approach using artificial neural network

BACKGROUND: Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social...

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Autores principales: Ahmadi, Maryam, Nopour, Raoof
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652806/
https://www.ncbi.nlm.nih.gov/pubmed/36371224
http://dx.doi.org/10.1186/s12911-022-02044-9
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author Ahmadi, Maryam
Nopour, Raoof
author_facet Ahmadi, Maryam
Nopour, Raoof
author_sort Ahmadi, Maryam
collection PubMed
description BACKGROUND: Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors. METHODS: In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL. RESULTS: Based on the BLR, the 13 factors were considered the best factors affecting the elderly’s QoL at P < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity  =  0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly. CONCLUSION: The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.
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spelling pubmed-96528062022-11-14 Clinical decision support system for quality of life among the elderly: an approach using artificial neural network Ahmadi, Maryam Nopour, Raoof BMC Med Inform Decis Mak Research BACKGROUND: Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors. METHODS: In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL. RESULTS: Based on the BLR, the 13 factors were considered the best factors affecting the elderly’s QoL at P < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity  =  0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly. CONCLUSION: The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly. BioMed Central 2022-11-12 /pmc/articles/PMC9652806/ /pubmed/36371224 http://dx.doi.org/10.1186/s12911-022-02044-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ahmadi, Maryam
Nopour, Raoof
Clinical decision support system for quality of life among the elderly: an approach using artificial neural network
title Clinical decision support system for quality of life among the elderly: an approach using artificial neural network
title_full Clinical decision support system for quality of life among the elderly: an approach using artificial neural network
title_fullStr Clinical decision support system for quality of life among the elderly: an approach using artificial neural network
title_full_unstemmed Clinical decision support system for quality of life among the elderly: an approach using artificial neural network
title_short Clinical decision support system for quality of life among the elderly: an approach using artificial neural network
title_sort clinical decision support system for quality of life among the elderly: an approach using artificial neural network
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652806/
https://www.ncbi.nlm.nih.gov/pubmed/36371224
http://dx.doi.org/10.1186/s12911-022-02044-9
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