Developing a prediction model for successful aging among the elderly using machine learning algorithms

OBJECTIVE: The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)(1) concept. It is believed that the SA prediction model can increase the quality of life (QoL)(2) in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. METHODS: The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB(3), XG-Boost J-48, RF(4), artificial neural network(5), support vector machine(6), and NB(7) algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)(8), negative predictive value (NPV)(9), sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). RESULTS: Comparing the machine learning(10) model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. CONCLUSIONS: Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.