Prediction of successful aging using ensemble machine learning algorithms

Varzaneh, Zahra Asghari; Yazdani, Azita; Kazemi-Arpanahi, Hadi

doi:10.1186/s12911-022-02001-6

Cited by 7 publications

(2 citation statements)

References 71 publications

(85 reference statements)

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“…ML allows an hypothesis-free datamining, instead of an hypothesis-driven data testing (Hägg et al 2019 ). Given these advantages the application of these models to ongoing ageing cohorts is being implemented more routinely (Gomez-Cabrero et al 2021 ; Speiser et al 2021 ; Varzaneh et al 2022 ).…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Measuring healthy ageing: current and future tools

et al. 2023

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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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Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Measuring healthy ageing: current and future tools

et al. 2023

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“…The easiest way to deploy an ML model is to create a web service for prediction. Therefore, in our future study, we will deploy the SA prediction model developed in this research as a cloud service available for elderly health monitoring clinics [ 75 , 95 , 96 ].…”

Section: Discussionmentioning

confidence: 99%

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

Yazdani,

Shanbehzadeh,

Kazemi-Arpanahi

2023

BMC Med Inform Decis Mak

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Introduction The 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. Methods In 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. Results The 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. Conclusions The 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.

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Divorce prediction using machine learning algorithms in Ha’il region, KSA

Moumen,

Shafqat,

Alraqad

et al. 2024

Sci Rep

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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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Prediction of successful aging using ensemble machine learning algorithms

Cited by 7 publications

References 71 publications

Measuring healthy ageing: current and future tools

Measuring healthy ageing: current and future tools

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

Divorce prediction using machine learning algorithms in Ha’il region, KSA

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