Developing an intelligent prediction system for successful aging based on artificial neural networks.

Background: Due to the growing number of disabilities in elderly, Attention to this period of life is essential to be considered. Few studies focused on the physical, mental, disabilities, and disorders affecting the quality of life in elderly people. SA¹ is related to various factors influencing the elderly's life. So, the objective of the current study is to build an intelligent system for SA prediction through ANN² algorithms to investigate better all factors affecting the elderly life and promote them.

Methods: This study was performed on 1156 SA and non-SA cases. We applied statistical feature reduction method to obtain the best factors predicting the SA. Two models of ANNs with 5, 10, 15, and 20 neurons in hidden layers were used for model construction. Finally, the best ANN configuration was obtained for predicting the SA using sensitivity, specificity, accuracy, and cross-entropy loss function.

Results: The study showed that 25 factors correlated with SA at the statistical level of P < 0.05. Assessing all ANN structures resulted in FF-BP³ algorithm having the configuration of 25-15-1 with accuracy-train of 0.92, accuracy-test of 0.86, and accuracy-validation of 0.87 gaining the best performance over other ANN algorithms.

Conclusions: Developing the CDSS for predicting SA has crucial role to effectively inform geriatrics and health care policymakers decision making.