Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms

BMC Medical Informatics and Decision Making

Research

Hadi Kazemi-Arpanahi¹  Mostafa Shanbehzadeh²  Azita Yazdani³ 

[1] Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran;Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran;Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran;Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran;Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran;

关键词: Machine learning;    Artificial intelligence;    Neural networks;    Fuzzy logic;    Successful aging;   

DOI  :  10.1186/s12911-023-02335-9

 received in 2022-06-02, accepted in 2023-10-10,  发布年份 2023

来源: Springer

【 摘 要 】

IntroductionThe global society is currently facing a rise in the elderly population. The concept of successful aging (SA) appeared in the gerontological literature to overcome the challenges and problems of population aging. SA is a subjective and multidimensional concept with many ambiguities regarding its meaning or measuring. This study aimed to propose an intelligent predictive model to predict SA.MethodsIn this retrospective study, the data of 784 elderly people were used to develop and validate machine learning (ML) methods. Data pre-processing was first performed. First, an adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict SA. Then, the predictive performance of the proposed model was compared with three ML algorithms, including multilayer perceptron (MLP) neural network, support vector machine (SVM), and random forest (RF) based on accuracy, sensitivity, precision, and F-score metrics.ResultsThe findings indicated that the ANFIS model with gauss2mf built-in membership function (MF) outperformed the other models with accuracy, sensitivity, precision, and F-score of 91.57%, 95.18%, 92.31%, and 92.94%, respectively.ConclusionsThe predictive performance of ANFIS is more efficient than the other ML models in SA prediction. The development of a decision support system (DSS) using our prediction model can provide healthcare administrators and policymakers with a reliable and responsive tool to improve elderly outcomes.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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