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

IF 1.7 Q2 MEDICINE, GENERAL & INTERNAL

International Journal of Preventive Medicine Pub Date : 2024-02-29 eCollection Date: 2024-01-01 DOI:10.4103/ijpvm.ijpvm_47_23

Raoof Nopour, Hadi Kazemi-Arpanahi

{"title":"Developing an intelligent prediction system for successful aging based on artificial neural networks.","authors":"Raoof Nopour, Hadi Kazemi-Arpanahi","doi":"10.4103/ijpvm.ijpvm_47_23","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":14342,"journal":{"name":"International Journal of Preventive Medicine","volume":"15 ","pages":"10"},"PeriodicalIF":1.7000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10982733/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Preventive Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/ijpvm.ijpvm_47_23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

开发基于人工神经网络的成功老龄化智能预测系统。

背景：由于老年人的残疾数量不断增加，对这一生命阶段的关注至关重要。很少有研究关注影响老年人生活质量的身体、精神、残疾和失调问题。SA1 与影响老年人生活的各种因素有关。因此，本研究的目的是通过 ANN2 算法建立一个用于预测 SA 的智能系统，以更好地调查影响老年人生活的各种因素并促进其发展：本研究以 1156 例 SA 和非 SA 病例为对象。方法：本研究以 1156 例 SA 和非 SA 病例为研究对象，采用统计特征还原法获得预测 SA 的最佳因素。在构建模型时，我们使用了两种隐层分别为 5、10、15 和 20 个神经元的 ANN 模型。最后，利用灵敏度、特异度、准确度和交叉熵损失函数得出了预测 SA 的最佳 ANN 配置：研究表明，25 个因素与 SA 的相关性达到 P < 0.05 的统计学水平。对所有 ANN 结构进行评估后，FF-BP3 算法的配置为 25-15-1，其训练准确率为 0.92，测试准确率为 0.86，验证准确率为 0.87，与其他 ANN 算法相比性能最佳：结论：开发用于预测 SA 的 CDSS 对老年医学和医疗决策者的决策具有重要作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Preventive Medicine MEDICINE, GENERAL & INTERNAL-

CiteScore

3.20

自引率

4.80%

发文量

107

期刊介绍： International Journal of Preventive Medicine, a publication of Isfahan University of Medical Sciences, is a peer-reviewed online journal with Continuous print on demand compilation of issues published. The journal’s full text is available online at http://www.ijpvmjournal.net. The journal allows free access (Open Access) to its contents and permits authors to self-archive final accepted version of the articles on any OAI-compliant institutional / subject-based repository. The journal will cover technical and clinical studies related to health, ethical and social issues in field of Preventive Medicine. Articles with clinical interest and implications will be given preference.