Unveiling Quality of Life Factors for the Elderly: A Public Health Nursing Approach Enhanced by Advanced ML and DL Techniques.

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.