Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?
Abstract 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 representin...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-023-01140-9 |
| _version_ | 1797557637037948928 |
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| author | Razieh Mirzaeian Raoof Nopour Zahra Asghari Varzaneh Mohsen Shafiee Mostafa Shanbehzadeh Hadi Kazemi-Arpanahi |
| author_facet | Razieh Mirzaeian Raoof Nopour Zahra Asghari Varzaneh Mohsen Shafiee Mostafa Shanbehzadeh Hadi Kazemi-Arpanahi |
| author_sort | Razieh Mirzaeian |
| collection | DOAJ |
| description | Abstract 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), Naïve 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. |
| first_indexed | 2024-03-10T17:20:34Z |
| format | Article |
| id | doaj.art-92a436d168ed432c8cf0d1b850124586 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-03-10T17:20:34Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-92a436d168ed432c8cf0d1b8501245862023-11-20T10:22:35ZengBMCBioMedical Engineering OnLine1475-925X2023-08-0122112510.1186/s12938-023-01140-9Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?Razieh Mirzaeian0Raoof Nopour1Zahra Asghari Varzaneh2Mohsen Shafiee3Mostafa Shanbehzadeh4Hadi Kazemi-Arpanahi5Department of Health Information Management, Shahrekord University of Medical SciencesStudent Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical SciencesDepartment of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of KermanDepartment of Nursing, Abadan University of Medical SciencesDepartment of Health Information Technology, School of Paramedical, Ilam University of Medical SciencesDepartment of Health Information Technology, Abadan University of Medical SciencesAbstract 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), Naïve 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.https://doi.org/10.1186/s12938-023-01140-9Machine learningData miningQuality of lifeHealth-related quality of lifeAgedSuccessful aging |
| spellingShingle | Razieh Mirzaeian Raoof Nopour Zahra Asghari Varzaneh Mohsen Shafiee Mostafa Shanbehzadeh Hadi Kazemi-Arpanahi Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? BioMedical Engineering OnLine Machine learning Data mining Quality of life Health-related quality of life Aged Successful aging |
| title | Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? |
| title_full | Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? |
| title_fullStr | Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? |
| title_full_unstemmed | Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? |
| title_short | Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? |
| title_sort | which are best for successful aging prediction bagging boosting or simple machine learning algorithms |
| topic | Machine learning Data mining Quality of life Health-related quality of life Aged Successful aging |
| url | https://doi.org/10.1186/s12938-023-01140-9 |
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