Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?

Thu Jul 25 20:26:34 2024

(2023) Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? Biomedical engineering online. p. 85. ISSN 1475-925X (Electronic) 1475-925X (Linking)

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BACKGROUND: The worldwide society is currently facing an epidemiological shift due to the significant improvement in life expectancy and increase in the elderly population. This shift requires the public and scientific community to highlight successful aging (SA), as an indicator representing the quality of elderly people's health. SA is a subjective, complex, and multidimensional concept; thus, its meaning or measuring is a difficult task. This study seeks to identify the most affecting factors on SA and fed them as input variables for constructing predictive models using machine learning (ML) algorithms. METHODS: Data from 1465 adults aged >/= 60 years who were referred to health centers in Abadan city (Iran) between 2021 and 2022 were collected by interview. First, binary logistic regression (BLR) was used to identify the main factors influencing SA. Second, eight ML algorithms, including adaptive boosting (AdaBoost), bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XG-Boost), random forest (RF), J-48, multilayered perceptron (MLP), Naive Bayes (NB), and support vector machine (SVM), were trained to predict SA. Finally, their performance was evaluated using metrics derived from the confusion matrix to determine the best model. RESULTS: The experimental results showed that 44 factors had a meaningful relationship with SA as the output class. In total, the RF algorithm with sensitivity = 0.95 +/- 0.01, specificity = 0.94 +/- 0.01, accuracy = 0.94 +/- 0.005, and F-score = 0.94 +/- 0.003 yielded the best performance for predicting SA. CONCLUSIONS: Compared to other selected ML methods, the effectiveness of the RF as a bagging algorithm in predicting SA was significantly better. Our developed prediction models can provide, gerontologists, geriatric nursing, healthcare administrators, and policymakers with a reliable and responsive tool to improve elderly outcomes.

Item Type: Article
Asghari Varzaneh, Z.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Adult Humans Aged Bayes Theorem *Algorithms *Random Forest Aging Machine Learning Data mining Health-related quality of life Quality of life Successful aging protocol. The protocol includes information regarding funding, sponsors (funded), institutional affiliations, potential conflicts of interest, incentives for subjects, and information regarding provisions for treating and/or compensating subjects who are harmed as a consequence of participation in the research study. This protocol is as follows: The authors declare that we have no significant competition for financial, professional, or personal interests that might have influenced the performance or presentation of the work described in this manuscript.
Page Range: p. 85
Journal or Publication Title: Biomedical engineering online
Journal Index: Pubmed
Volume: 22
Number: 1
Identification Number:
ISSN: 1475-925X (Electronic) 1475-925X (Linking)
Depositing User: مهندس مهدی شریفی

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