Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests

Cai, TianPan; Long, Jingwen; Kuang, Jie; Fu, You; Zou, Tingting; Wu, Lei

doi:10.1111/ggi.13926

Cited by 14 publications

(19 citation statements)

References 26 publications

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“…Memory is ubiquitous in people's lives as a process that enables the storage of experience; for example, the acquisition of life experience and how to learn to use household appliances are all things that memory supports you to achieve [6]. In sports, memory is associated with the acquisition of motor skills.…”

Section: Current Status Of Researchmentioning

confidence: 99%

Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

Zhao

Wang

Fan

2021

Scientific Programming

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A deep learning approach is used in this study to provide insight into aerobics movement recognition, and the model is used for aerobics movement recognition. The model complexity is significantly reduced, while the multi-scale features of the target at the fine-grained level are extracted, significantly improving the characterization of the target, by embedding lightweight multi-scale convolution modules in 3D convolutional residual networks to increase the local perceptual field range in each layer of the network. Finally, using the channel attention mechanism, the key features are extracted from the multi-scale features. To create a dual-speed frame rate detection model, the fast-slow combination idea is fused into a 3D convolutional network. To obtain spatial semantic information and motion information in the video, the model uses different frame rates, and the two-channel information is fused with features using lateral concatenation. Following the acquisition of all features, the features are fed into a temporal detection network to identify temporal actions and to design a behavior recognition system for the network model to demonstrate the network model's applicability. The average scores of students in the experimental group were significantly higher than those in the control group in seven areas: set accuracy, movement amplitude, movement strength, body coordination, coordination of movement and music, movement expression, and aesthetics; the average scores of movement proficiency and body control in the experimental group were also significantly higher than those in the control group, but the differences were not significant. The differences between the eight indicators in the experimental group were not significant when compared to those in the preexperimental group, indicating that intensive rhythm training for students improves secondary school students' comprehension, proficiency, and presentation of aerobics sets.

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Section: Current Status Of Researchmentioning

confidence: 99%

Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

Zhao

Wang

Fan

2021

Scientific Programming

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“…In other words, they have focused on the predictive models in the SA in the elderly based on more physical factors and paid less other factors affecting it specially attention to the social factors in this respect. 27 , 28 Social interactions for the elderly are crucial and should be considered in SA. 29 Therefore, this study aimed to develop the prediction model using ML algorithms for SA among the elderly by embedding social factors in this respect.…”

Section: Introductionmentioning

confidence: 99%

Developing a prediction model for successful aging among the elderly using machine learning algorithms

2023

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Objective The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. Methods The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). Results Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. Conclusions Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

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“…Prior investigations have generally focused on factors affecting SA, but there are no longitudinal studies on SA [ 24 , 25 ]. The factors affecting SA are codependent and multifaceted, and conventional statistical models are not appropriate for this concept [ 26 ]. Over the past few decades, machine learning (ML) algorithms have played a key role in solving complex, multidimensional, and nonlinear problems [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of successful aging using ensemble machine learning algorithms

Varzaneh

Yazdani

Kazemi-Arpanahi³

2022

BMC Med Inform Decis Mak

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Background Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive and qualitative view of aging. From a biomedical perspective, SA is defined as the absence of diseases or disability disorders. This is distinct from normal aging, which is associated with age-related deterioration in physical and cognitive functions. From a social perspective, SA highlights life satisfaction and individual well-being, usually attained through socialization. It is an abstract and multidimensional concept surrounded by imprecision about its definition and measurement. Our study attempted to find the most effective features of SA as defined by Rowe and Kahn's theory. The determined features were used as input parameters of six machine learning (ML) algorithms to create and validate predictive models for SA. Methods In this retrospective study, the raw data set was first pre-processed; then, based on the data of a sample of 983, five basic ML techniques including artificial neural network, decision tree, support vector machine, Naïve Bayes, and k-nearest neighbors (K-NN) with one ensemble method (that gathers 30 K-NN algorithms as weak learners) were trained. Finally, the prediction result was yielded using the majority vote method based on the output of the generated base models. Results The experimental results revealed that the predictive system has been more successful in predicting SA with a 93% precision, 92.40% specificity, 87.80% sensitivity, 90.31% F-measure, 89.62% accuracy, and a ROC of 96.10%, using a five-fold cross-validation procedure. Conclusions Our results showed that ML techniques potentially have satisfactory performance in supporting the SA-related decisions of social and health policymakers. The KNN-based ensemble algorithm is superior to the other ML models in classifying people into SA and non-SA classes.

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Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests

Cited by 14 publications

References 26 publications

Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

Developing a prediction model for successful aging among the elderly using machine learning algorithms

Prediction of successful aging using ensemble machine learning algorithms

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