Unveiling Quality of Life Factors for the Elderly: A Public Health Nursing Approach Enhanced by Advanced ML and DL Techniques.
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| Titel: | Unveiling Quality of Life Factors for the Elderly: A Public Health Nursing Approach Enhanced by Advanced ML and DL Techniques. |
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| Autoren: | Devi S; Symbiosis College of Nursing (SCON), Symbiosis International Deemed University (SIDU), Pune, India., Yadav R; Department of Artificial Intelligence, Symbiosis Institute of Technology (SIT), Symbiosis International Deemed University (SIDU), Pune, India., Chavan R; Symbiosis College of Nursing (SCON), Symbiosis International Deemed University (SIDU), Pune, India., Gangarde R; Department of Artificial Intelligence, Symbiosis Institute of Technology (SIT), Symbiosis International Deemed University (SIDU), Pune, India. |
| Quelle: | Public health nursing (Boston, Mass.) [Public Health Nurs] 2025 Nov-Dec; Vol. 42 (6), pp. 1850-1869. Date of Electronic Publication: 2025 Jul 06. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Blackwell Scientific Publications Country of Publication: United States NLM ID: 8501498 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1525-1446 (Electronic) Linking ISSN: 07371209 NLM ISO Abbreviation: Public Health Nurs Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Boston, MA] : Blackwell Scientific Publications, [c1984- |
| MeSH-Schlagworte: | Quality of Life*/psychology , Public Health Nursing*/methods , Machine Learning* , Artificial Intelligence*, Humans ; Aged ; Male ; Female ; Aged, 80 and over ; Deep Learning ; Middle Aged ; Exercise/psychology |
| Abstract: | Community health nurses can enhance the elderly's quality of life (QoL) through personalized care, lifestyle counselling, and preventive measures. The primary objective of this study was to develop artificial intelligence (AI)-based prediction models to identify the key influencing factors that can impact the QoL in the elderly population. The estimated sample size was 500, and participants were selected using a systematic sampling technique. The pre-processing stage was applied to the primary dataset. Following this, basic machine learning (ML), deep learning (DL), and ensemble models were implemented to predict QoL. The SMOTE method was applied to balance the dataset. AdaBoost was the best-performing model, achieving an accuracy of 93.7%, with excellent recall (96.8%) and specificity (96.8%). Physical activity (48.9%) and daily activity ability (30.8%) were key QoL predictors, while regression analysis revealed physical activity (coefficient: 1.2260, p < 0.001) as a positive contributor. AI approaches help the community health nurses to predict the factors required for improving QoL early on, enabling them to provide the elderly population with the appropriate advice and future plans to manage aging challenges. (© 2025 Wiley Periodicals LLC.) |
| References: | Abbas, M., K. A. Memon, A. A. Jamali, S. Memon, and A. Ahmed. 2019. “Multinomial Naive Bayes Classification Model for Sentiment Analysis.” International Journal of Computer Science and Network Security 19: 62. Ahmadi, M., R. Nopour, and S. Nasiri. 2023. “Developing a Prediction Model for Successful Aging Among the Elderly Using Machine Learning Algorithms.” Digital Health 9: 20552076231178425. AL‐Behadili, H. N. K. 2021. “Decision Tree for Multiclass Classification of Firewall Access.” International Journal of Intelligent Engineering and Systems 14: 294–302. Asghari Varzaneh, Z., M. Shanbehzadeh, and H. Kazemi‐Arpanahi. 2022. “Prediction of Successful Aging Using Ensemble Machine Learning Algorithms.” BMC Medical Informatics and Decision Making 22: 258. Bachri, O. S., M. H. Kusnadi, and O. D. Nurhayati. 2017. “Feature Selection Based on Chi Square in Artificial Neural Network to Predict the Accuracy of Student Study Period.” International Journal of Civil Engineering and Technology 8: 731–739. Bikia, V., T. Fong, R. E. Climie, et al. 2021. “Leveraging the Potential of Machine Learning for Assessing Vascular Ageing: State‐of‐the‐Art and Future Research.” European Heart Journal ‐ Digital Health 2: 676–690. Bowling, A., and P. Dieppe. 2005. “What Is Successful Ageing and Who Should Define It?” British Medical Journal 331: 1548–1551. Byeon, H. 2021. “Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population‐Based Study.” International Journal of Environmental Research and Public Health 18: 7625. Caballero, F. F., G. Soulis, W. Engchuan, et al. 2017. “Advanced Analytical Methodologies for Measuring Healthy Ageing and Its Determinants, Using Factor Analysis and Machine Learning Techniques: The Athlos Project.” Scientific Reports 7: 43955. Cai, T., J. Long, J. Kuang, F. You, T. Zou, and L Wu. 2020. “Applying Machine Learning Methods to Develop a Successful Aging Maintenance Prediction Model Based on Physical Fitness Tests.” Geriatrics & Gerontology International 20: 637–642. Chandraa, C. E., and S. Abdullaha. 2022. “Forecasting Mortality Trend of Indonesian Old Aged Population With Bayesian Method.” International Journal on Advanced Science, Engineering and Information Technology 12: 580–588. Chen, X., D. Yu, X. Fan, L. Wang, and J. Chen. 2021. “Multiclass Classification for Self‐Admitted Technical Debt Based on XGBoost.” IEEE Transactions on Reliability 71: 1309–1324. Estebsari, F., M. Dastoorpoor, Z. R. Khalifehkandi, et al. 2020. “The Concept of Successful Aging: A Review Article.” Current Aging Science 13: 4–10. Féraud, R., and F. Clérot. 2002. “A Methodology to Explain Neural Network Classification.” Neural Networks 15: 237–246. Fernández, A., S. Garcia, F. Herrera, and N. V. Chawla. 2018. “Smote for Learning From Imbalanced Data: Progress and Challenges, Marking the 15‐Year Anniversary.” Journal of Artificial Intelligence Research 61: 863–905. Ferrucci, L., M. Gonzalez‐Freire, E. Fabbri, et al. 2020. “Measuring Biological Aging in Humans: A Quest.” Aging Cell 19: e13080. Galiana, L., M. Gutiérrez, P. Sancho, E.‐H. Francisco, and J. M. Tomás. 2016. “Socio‐Demographic Variables and Successful Aging of the Angolan Elderly.” Scientifica 2016: 5306756. Hossin, M., and M. N. Sulaiman. 2015. “A Review on Evaluation Metrics for Data Classification Evaluations.” International Journal of Data Mining & Knowledge Management Process 5: 1. Hota, S., and S. Pathak. 2018. “KNN Classifier Based Approach for Multi‐Class Sentiment Analysis of Twitter Data.” International Journal of Engineering and Technology 7: 1372–1375. Javed, Y., and N. Rajabi. 2020. “Multi‐Layer Perceptron Artificial Neural Network Based IoT Botnet Traffic Classification.” In Proceedings of the Future Technologies Conference (FTC) 2019: Volume 1, 973–984. Springer. Kim, H., B. L. Drake, and H. Park. 2007. “Multiclass Classifiers Based on Dimension Reduction With Generalized LDA.” Pattern Recognition 40: 2939–2945. Kim, H.‐C., S. Pang, H.‐M. Je, D. Kim, and S.‐Y. Bang. 2002. “Support Vector Machine Ensemble With Bagging.” In Pattern Recognition With Support Vector Machines: First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings, 397–408. Springer. Lee, E. S. 2017. “Exploring the Performance of Stacking Classifier to Predict Depression Among the Elderly.” In 2017 IEEE International Conference on Healthcare Informatics (ICHI), 13–20. IEEE. Lee, S.‐H., I. Choi, W. Ahn, et al. 2020. “Estimating Quality of Life With Biomarkers Among Older Korean Adults: A Machine‐Learning Approach.” Archives of Gerontology and Geriatrics 87: 103966. Liaqat, S., K. Dashtipour, S. A. Shah, et al. 2021. “Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices.” IEEE Sensors Journal 21: 18214–18221. Lin, E., C.‐H. Lin, and H.‐Y. Lane. 2021. “Prediction of Functional Outcomes of Schizophrenia With Genetic Biomarkers Using a Bagging Ensemble Machine Learning Method With Feature Selection.” Scientific Reports 11: 10179. Lin, L., H. H. Wang, C. Lu, W. Chen, and V. Y. Guo. 2021. “Adverse Childhood Experiences and Subsequent Chronic Diseases Among Middle‐Aged or Older Adults in China and Associations With Demographic and Socioeconomic Characteristics.” JAMA Network Open 4: e2130143. Mayoraz, E., and E. Alpaydin. 1999. “Support Vector Machines for Multi‐Class Classification.” In International Work‐Conference on Artificial Neural Networks, 833–842. Springer. Michel, J.‐P., and R. Sadana. 2017. ““Healthy Aging” Concepts and Measures.” Journal of the American Medical Directors Association 18: 460–464. Mienye, I. D., Y. Sun, and Z. Wang. 2020. “An Improved Ensemble Learning Approach for the Prediction of Heart Disease Risk.” Informatics Medicine Unlocked 20: 100402. Mirzaeian, R., R. Nopour, Z. Asghari Varzaneh, M. Shafiee, M. Shanbehzadeh, and H. Kazemi‐Arpanahi. 2023. “Which Are Best for Successful Aging Prediction? Bagging, Boosting, or Simple Machine Learning Algorithms?” Biomed Engineering Online 22: 85. Nosraty, L., J. Pulkki, J. Raitanen, L. Enroth, and M. Jylhä. 2019. “Successful Aging as a Predictor of Long‐Term Care Among Oldest Old: The Vitality 90+ Study.” Journal of Applied Gerontology 38: 553–571. Paul, R., T. Tsuei, K. Cho, et al. 2021. “Ensemble Machine Learning Classification of Daily Living Abilities Among Older People With HIV.” EClinicalMedicine 35: 1–9. Peng, T., X. Chen, M. Wan, et al. 2021. “The Prediction of Hepatitis E Through Ensemble Learning.” International Journal of Environmental Research and Public Health 18: 159. Polikar, R. 2012. “Ensemble Learning.” In Ensemble Machine Learning: Methods Applications, 1–34. Springer. Raza, K. 2019. “Improving the Prediction Accuracy of Heart Disease With Ensemble Learning and Majority Voting Rule.” In U‐Healthcare Monitoring Systems, 179–196. Elsevier. Rowe, J. W., and R. L. Kahn. 1997. “Successful Aging.” Gerontologist 37: 433–440. Seong, M. H., E. Shin, and S. Sok. 2021. “Successful Aging Perception in Middle‐Aged Korean Men: A Q Methodology Approach.” International Journal of Environmental Research and Public Health 18: 3095. Sesmero, M. P., A. I. Ledezma, and A. Sanchis. 2015. “Generating Ensembles of Heterogeneous Classifiers Using Stacked Generalization.” Wiley Interdisciplinary Reviews: Data Mining Knowledge Discovery 5: 21–34. Shah, V. R., D. S. Christian, A. C. Prajapati, M. M. Patel, and K. Sonaliya. 2017. “Quality of Life Among Elderly Population Residing in Urban Field Practice Area of a Tertiary Care Institute of Ahmedabad City, Gujarat.” Journal of Family Medicine and Primary Care 6: 101–105. Shen, X., F. Yin, and C. Jiao. 2023. “Predictive Models of Life Satisfaction in Older People: A Machine Learning Approach.” International Journal of Environmental Research and Public Health 20: 2445. Shen, Y., M. A. Hossain, and S. K. Ray. 2021. “Supporting Elderly People During Medical Emergencies: An Informal Caregiver‐Based Approach.” In 2021 IEEE Symposium on Computers and Communications (ISCC), 1–6. IEEE. Verleysen, M., and D. François. 2005. “The Curse of Dimensionality in Data Mining and Time Series Prediction.” In International Work‐Conference on Artificial Neural Networks, 758–770. Springer. Wagg, E., F. M. Blyth, R. G. Cumming, and S. Khalatbari‐Soltani. 2021. “Socioeconomic Position and Healthy Ageing: A Systematic Review of Cross‐Sectional and Longitudinal Studies.” Ageing Research Reviews 69: 101365. Wong, P.‐H., K. Kourtit, and P. Nijkamp. 2021. “The Ideal Neighbourhoods of Successful Ageing: A Machine Learning Approach.” Heal & Place 72: 102704. World Health Organization. 2011. Global Health and Aging, 2. US National Institute of Aging. Young, Y., K. D. Frick, and E. A. Phelan. 2009. “Can Successful Aging and Chronic Illness Coexist in the Same Individual? A Multidimensional Concept of Successful Aging.” Journal of the American Medical Directors Association 10: 87–92. Yu, T., and H. Zhu. 2020. Hyper‐Parameter Optimization: A Review of Algorithms and Applications. arXiv preprint arXiv:2003.1–56. Zhou, Z. 2020. “The Application of Machine Learning in Activity Recognition With Healthy Older People Using a Batteryless Wearable Sensor.” In Proceedings of the 4th International Conference on Advances in Artificial Intelligence, 1–8. |
| Contributed Indexing: | Keywords: deep learning; ensemble models; machine learning; public health nurse; quality of life; successful aging factors |
| Entry Date(s): | Date Created: 20250706 Date Completed: 20251106 Latest Revision: 20251106 |
| Update Code: | 20251106 |
| DOI: | 10.1111/phn.70003 |
| PMID: | 40619584 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Community health nurses can enhance the elderly's quality of life (QoL) through personalized care, lifestyle counselling, and preventive measures. The primary objective of this study was to develop artificial intelligence (AI)-based prediction models to identify the key influencing factors that can impact the QoL in the elderly population. The estimated sample size was 500, and participants were selected using a systematic sampling technique. The pre-processing stage was applied to the primary dataset. Following this, basic machine learning (ML), deep learning (DL), and ensemble models were implemented to predict QoL. The SMOTE method was applied to balance the dataset. AdaBoost was the best-performing model, achieving an accuracy of 93.7%, with excellent recall (96.8%) and specificity (96.8%). Physical activity (48.9%) and daily activity ability (30.8%) were key QoL predictors, while regression analysis revealed physical activity (coefficient: 1.2260, p < 0.001) as a positive contributor. AI approaches help the community health nurses to predict the factors required for improving QoL early on, enabling them to provide the elderly population with the appropriate advice and future plans to manage aging challenges.<br /> (© 2025 Wiley Periodicals LLC.) |
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| ISSN: | 1525-1446 |
| DOI: | 10.1111/phn.70003 |