Ahmadi Maryam, Nopour Raoof, Nasiri Somayeh
Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
Digit Health. 2023 May 29;9:20552076231178425. doi: 10.1177/20552076231178425. eCollection 2023 Jan-Dec.
The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA) concept. It is believed that the SA prediction model can increase the quality of life (QoL) 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.
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, XG-Boost J-48, RF, artificial neural network, support vector machine, and NB algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC).
Comparing the machine learning 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.
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.
老龄化现象在全球呈上升趋势，这促使了成功老龄化（SA）概念的出现。人们认为，SA预测模型可以通过减少身心问题并增强老年人的社会参与度来提高其生活质量（QoL）。以往大多数研究指出，身心障碍会影响老年人的生活质量，但在这方面对社会因素关注不多。我们的研究旨在基于影响SA的身体、心理，特别是更多社会因素构建一个SA预测模型。
本研究调查了975例与老年人SA和非SA相关的病例。我们使用单因素分析来确定影响SA的最佳因素。使用AB、XG-Boost J-48、随机森林（RF）、人工神经网络、支持向量机和朴素贝叶斯（NB）算法构建预测模型。为了获得预测SA的最佳模型，我们使用阳性预测值（PPV）、阴性预测值（NPV）、灵敏度、特异度、准确度、F值和受试者工作特征曲线下面积（AUC）对它们进行比较。
比较机器学习模型的性能表明，随机森林（RF）模型的PPV = 90.96%，NPV = 99.21%，灵敏度 = 97.48%，特异度 = 97.14%，准确度 = 97.05%，F值 = 97.31%，AUC = 0.975，是预测SA的最佳模型。
使用预测模型可以提高老年人的生活质量，从而降低个人和社会的经济成本。随机森林（RF）可被视为预测老年人SA的最佳模型。
