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Moumen A, Shafqat A, Alraqad T, Alshawarbeh ES, Saber H, Shafqat R. Divorce prediction using machine learning algorithms in Ha'il region, KSA. Sci Rep 2024; 14:502. [PMID: 38177210 PMCID: PMC10766631 DOI: 10.1038/s41598-023-50839-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 12/26/2023] [Indexed: 01/06/2024] Open
Abstract
The application of artificial intelligence (AI) in predictive analytics is growing in popularity. It has the power to offer ground-breaking solutions for a range of social problems and real world societal difficulties. It is helpful in addressing some of the social issues that today's world seems incapable of solving. One of the most significant phenomena affecting people's lives is divorce. The goal of this paper is to study the use of machine learning algorithms to determine the effectiveness of divorce predictor scale (DPS) and identify the reasons that usually lead to divorce in the scenario of Hail region, KSA. For this purpose, in this study, the DPS, based on Gottman couples therapy, was used to predict divorce by applying different machine learning algorithms. There were 54 items of the DPS used as features or attributes for data collection. In addition to the DPS, a personal information form was utilized to gather participants' personal data in order to conduct this study in a more structured and traditional manner. Out of 148 participants 116 participants were married whereas 32 were divorced. With the use of algorithms artificial neural network (ANN), naïve bayes (NB), and random forest (RF), the effectiveness of DPS was examined in this study. The correlation based feature selection method was used to identify the top six features from the same dataset and the highest accuracy rate was 91.66% with RF. The results show that DPS can predict divorce. This scale can help family counselors and therapists in case formulation and intervention plan development process. Additionally, it may be argued that the Hail region, KSA sampling confirmed the Gottman couples treatment predictors.
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Affiliation(s)
- Abdelkader Moumen
- Department of Mathematics, College of Science, University of Ha'il, Ha'il, 55473, Saudi Arabia.
| | - Ayesha Shafqat
- Department of Education, The University of Lahore, Sargodha, 40100, Pakistan
| | - Tariq Alraqad
- Department of Mathematics, College of Science, University of Ha'il, Ha'il, 55473, Saudi Arabia
| | - Etaf Saleh Alshawarbeh
- Department of Mathematics, College of Science, University of Ha'il, Ha'il, 55473, Saudi Arabia
| | - Hicham Saber
- Department of Mathematics, College of Science, University of Ha'il, Ha'il, 55473, Saudi Arabia
| | - Ramsha Shafqat
- Department of Mathematics and Statistics, The University of Lahore, Sargodha, 40100, Pakistan
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Silva N, Rajado AT, Esteves F, Brito D, Apolónio J, Roberto VP, Binnie A, Araújo I, Nóbrega C, Bragança J, Castelo-Branco P. Measuring healthy ageing: current and future tools. Biogerontology 2023; 24:845-866. [PMID: 37439885 PMCID: PMC10615962 DOI: 10.1007/s10522-023-10041-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 07/14/2023]
Abstract
Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.
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Affiliation(s)
- Nádia Silva
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Ana Teresa Rajado
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Filipa Esteves
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - David Brito
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Joana Apolónio
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Vânia Palma Roberto
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
| | - Alexandra Binnie
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Department of Critical Care, William Osler Health System, Etobicoke, ON, Canada
| | - Inês Araújo
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Clévio Nóbrega
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - José Bragança
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Pedro Castelo-Branco
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal.
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal.
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal.
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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Mirzaeian R, Nopour R, Asghari Varzaneh Z, Shafiee M, Shanbehzadeh M, Kazemi-Arpanahi H. Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms? Biomed Eng Online 2023; 22:85. [PMID: 37644599 PMCID: PMC10463617 DOI: 10.1186/s12938-023-01140-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/21/2023] [Indexed: 08/31/2023] Open
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.
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Affiliation(s)
- Razieh Mirzaeian
- Department of Health Information Management, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Raoof Nopour
- Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Mohsen Shafiee
- Department of Nursing, Abadan University of Medical Sciences, Abadan, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
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Silva N, Rajado AT, Esteves F, Brito D, Apolónio J, Roberto VP, Binnie A, Araújo I, Nóbrega C, Bragança J, Castelo-Branco P, Andrade RP, Calado S, Faleiro ML, Matos C, Marques N, Marreiros A, Nzwalo H, Pais S, Palmeirim I, Simão S, Joaquim N, Miranda R, Pêgas A, Sardo A. Measuring healthy ageing: current and future tools. Biogerontology 2023. [DOI: https:/doi.org/10.1007/s10522-023-10041-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 09/01/2023]
Abstract
AbstractHuman ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.
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Ahmadi M, Kazemi-Arpanahi H, Nopour R, Shanbehzadeh M. Factors influencing quality of life among the elderly: An approach using logistic regression. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:215. [PMID: 37545996 PMCID: PMC10402806 DOI: 10.4103/jehp.jehp_13_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/20/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Improving the physical, psychological, and social factors in the elderly significantly increases the QoL1 among them. This study aims to identify the crucial factors for predicting QoL among the elderly using statistical methods. MATERIALS AND METHODS In this study, 980 samples related to the elderly with favorable and unfavorable QoL were investigated. The elderly's QoL was investigated using a qualitative and self-assessment questionnaire that measured the QoL among them by five Likert spectrum and independent factors. The Chi-square test and eta coefficient were used to determine the relationship between each predicting factor of the elderly's QoL in SPSS V 25 software. Finally, we used the Enter and Forward LR methods to determine the correlation of influential factors in the presence of other variables. RESULTS The study showed that 20 variables gained a significant relationship with the quality of life of the elderly at P < 0.05. The study results showed that the degree of dependence (P = 0.03), diabetes mellitus (P = 0.03), formal and informal social relationships (P = 0.01 and P = 0.02), ability to play an emotional role (P = 0.03), physical performance (P = 0.01), heart diseases and arterial blood pressure (P = 0.02), and cancer (P = 0.01) have favorable predictive power in predicting the QoL among the elderly. CONCLUSION Attempts to identify and modify the important factors affecting the elderly's QoL have a significant role in improving the QoL and life satisfaction in this age group people. This study showed that the statistical methods have a pleasant capability to discover the factors associated with the elderly's QoL with high performance in this regard.
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Affiliation(s)
- Maryam Ahmadi
- Ph.D. of Health Information Management, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Ph.D. of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
| | - Raoof Nopour
- Ph.D. Student of Health Information Management, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- Ph.D. of Health Information Management, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
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