Clinical decision support system for quality of life among the elderly: an approach using artificial neural network
Abstract 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...
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| Format: | Article |
| Language: | English |
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BMC
2022-11-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-022-02044-9 |
| _version_ | 1798018194169921536 |
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| author | Maryam Ahmadi Raoof Nopour |
| author_facet | Maryam Ahmadi Raoof Nopour |
| author_sort | Maryam Ahmadi |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T16:20:01Z |
| format | Article |
| id | doaj.art-710777f929a34cd6b8e7f01dcb61795d |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-11T16:20:01Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-710777f929a34cd6b8e7f01dcb61795d2022-12-22T04:14:25ZengBMCBMC Medical Informatics and Decision Making1472-69472022-11-0122111510.1186/s12911-022-02044-9Clinical decision support system for quality of life among the elderly: an approach using artificial neural networkMaryam Ahmadi0Raoof Nopour1Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical SciencesDepartment of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical SciencesAbstract 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.https://doi.org/10.1186/s12911-022-02044-9ElderlyQuality of lifeClinical decision support systemArtificial neural networkMachine learning |
| spellingShingle | Maryam Ahmadi Raoof Nopour Clinical decision support system for quality of life among the elderly: an approach using artificial neural network BMC Medical Informatics and Decision Making Elderly Quality of life Clinical decision support system Artificial neural network Machine learning |
| 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 | Elderly Quality of life Clinical decision support system Artificial neural network Machine learning |
| url | https://doi.org/10.1186/s12911-022-02044-9 |
| work_keys_str_mv | AT maryamahmadi clinicaldecisionsupportsystemforqualityoflifeamongtheelderlyanapproachusingartificialneuralnetwork AT raoofnopour clinicaldecisionsupportsystemforqualityoflifeamongtheelderlyanapproachusingartificialneuralnetwork |