ArticleBMC medical informatics and decision making2022
Clinical decision support system for quality of life among the elderly: an approach using artificial neural network.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- An intuitionistic fuzzy automated negotiation model for personalized and efficient shared decision-making.Scientific reports · 2025Article
- Development of a Knowledge Base for an Integrated Older Adult Care Model (SMART System) Based on an Intervention Mapping Framework: Mixed Methods Study.JMIR nursing · 2025Article
- Development and evaluation of a clinical nursing decision support system for the prevention of neonatal hypoglycaemia.BMC medical informatics and decision making · 2024Article
- Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors.BMC medical informatics and decision making · 2024Article
- A novel explainable machine learning-based healthy ageing scale.BMC medical informatics and decision making · 2024Article
- Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study.Diagnostics (Basel, Switzerland) · 2024Article
- Global incidence, prevalence and disease burden of silicosis: 30 years' overview and forecasted trends.BMC public health · 2023Review
- Personalized Risk Analysis to Improve the Psychological Resilience of Women Undergoing Treatment for Breast Cancer: Development of a Machine Learning-Driven Clinical Decision Support Tool.Journal of medical Internet research · 2023Article
- Factors influencing quality of life among the elderly: An approach using logistic regression.Journal of education and health promotion · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue 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.
methodsIn 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.
resultsBased 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.
conclusionThe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.