Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model

Chen, Wenzheng; Yang, Guanglei; Qi, Dongkai

doi:10.1155/2022/5504850

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…It is important to note that DT is one of the powerful methods commonly used in various fields, such as ML, image processing, and pattern recognition. This is consistent with the conclusion in Chen et al [35] that DT has been implemented in many areas due to it simple analysis and precision on various forms of data [35].…”

Section:  Gain Ratiosupporting

confidence: 92%

Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer

Surono,

Afitian,

Setyawan

et al. 2023

HighTech. Innov. J.

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One of the best-known and frequently used areas of Deep Learning in image processing is the Convolutional Neural Network (CNN), which has architectural designs such as Inceptionv3, DenseNet201, Resnet50, and MobileNet used in image classification and pattern recognition. Furthermore, the CNN extracts feature from the image according to the designed architecture and performs classification through the fully connected layer, which executes the Machine Learning (ML) algorithm tasks. Examples of ML that are commonly used include Naive Bayes (NB), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Decision Tree (DT). This research was conducted based on an AI model development background and the need for a system to diagnose COVID-19 quickly and accurately. The aim was to classify the aforementioned CNN models with ML algorithms and compare the models’ accuracy before and after Bayesian optimization using CXR lung images with a total of 2000 data. Consequently, the CNN extracted 80% of the training data and 20% for testing, which was assigned to four different ML models for classification with the use of Bayesian optimization to ensure the best accuracy. It was observed that the best model classification was generated by the MobileNetV2-SVM structure with an accuracy of 93%. Therefore, the accuracy obtained using the SVM algorithm is higher than the other three ML algorithms. Doi: 10.28991/HIJ-2023-04-03-05 Full Text: PDF

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Section:  Gain Ratiosupporting

confidence: 92%

Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer

Surono,

Afitian,

Setyawan

et al. 2023

HighTech. Innov. J.

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“…The software IBM SPSS modeler was used to create a decision tree model (parameters to be set as follows: the maximum depth of the tree structure is 5, the minimum number of cases of nodes influencing the change in the physical fitness of toddlers is 100, the minimum number of instances of sub-nodes is 50, and the minimum change value of Gini coefficient is 0.0001), and a 10-level crossover was used to verify the accuracy of the model identification. The Chi-squared Automatic Interaction Detector (CHAID), also known as the chi-square automatic interaction detection algorithm, is a decision tree technique based on adjusted significance testing (Bonferroni test), uses the chi-squared test to evaluate the significance of variable grouping on the target variable and selects the splitting point that maximizes the chi-squared value as the optimal splitting point (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). Due to the characteristics of large sample size, multiple indicators, this study used the significance of chi-square tests to automatically determine the grouping variables and split values of the multivariate contingency table, thereby quickly and effectively identifying the main influencing factors.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…as these methods lack deep data mining and decision analysis and are vulnerable to covariance problems (21,22). In recent years, scholars have used decision trees in data mining techniques to conduct functional explorations in many fields (23)(24)(25)(26)(27)(28)(29)(30)(31)(32). As an important classification technique in data mining, decision trees are capable of effectively identifying the main influencing factors of physical changes.…”

Section: Introductionmentioning

confidence: 99%

A cohort study of factors influencing the physical fitness of preschool children: a decision tree analysis

Lv,

Fu,

Zhao

et al. 2023

Front. Public Health

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ObjectiveBased on the decision tree model, to explore the key influencing factors of children’s physical fitness, rank the key influencing factors, and explain the complex interaction between the influencing factors.MethodsA cohort study design was adopted. 1,276 children (ages 3–6) from 23 kindergartens in Nanchang, China, were chosen for the study to measure the children’s physical fitness at baseline and a year later and to compare the physical fitness scores at the two stages. The study was conducted following the Chinese National Physical Fitness Testing Standard (Children Part); To identify the primary influencing factors of changes in physical fitness, a decision tree model was developed, and a questionnaire survey on birth information, feeding patterns, SB, PA, dietary nutrition, sleep, parental factors, and other relevant information was conducted.ResultsThe levels of physical fitness indicators among preschool children showed a significant increase after 1 year. The accuracy of the CHAID model is 84.17%. It showed that 7 variables were strongly correlated with the physical changes of children’s fitness, the order of importance of each variable was weekend PA, weekend MVPA, mother’s BMI, mother’s sports frequency, father’s education, mother’s education, and school day PA. Three factors are related to PA. Four factors are related to parental circumstances. In addition to the seven important variables mentioned, variables such as breakfast frequency on school day, puffed food, frequency of outing, school day MVPA, parental feeling of sports, father’s occupation, and weekend breakfast frequency are all statistically significant leaf node variables.ConclusionPA, especially weekend PA, is the most critical factor in children’s physical fitness improvement and the weekend MVPA should be increased to more than 30 min/d based on the improvement of weekend PA. In addition, parental factors and school day PA are also important in making decisions about changes in fitness for children. The mother’s efforts to maintain a healthy BMI and engage in regular physical activity are crucial for enhancing the physical fitness of children. Additionally, other parental factors, such as the parents’ educational levels and the father’s occupation, can indirectly impact the level of physical fitness in children.

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“…In the sports, a Decision Support System (DSS) serves as a critical tool for coaches and trainers to make informed decisions regarding athlete training, performance optimization, and injury prevention [6]. Leveraging algorithms like the Iterative Dichotomiser 3 (ID3) algorithm within this system enhances its capability to process vast amounts of data and derive actionable insights.…”

Section: Introductionmentioning

confidence: 99%

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

Chen

2024

jes

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A Decision Support System (DSS) is a computer-based tool designed to assist individuals and organizations in making informed decisions. It utilizes data analysis, modeling techniques, and interactive interfaces to provide users with relevant information and insights. DSSs help streamline decision-making processes by synthesizing large amounts of data from various sources, generating forecasts, and evaluating alternative courses of action. They are particularly valuable in complex and uncertain situations where decisions have significant implications. DSSs can be used in a wide range of domains, including business, healthcare, finance, and logistics, to support strategic, tactical, and operational decision-making processes. This paper introduces an Integrated Hybrid Feature Subset Decision Support System (HFS-DSS) designed to optimize decision-making processes in student performance evaluation within educational institutions. The HFS-DSS combines feature subset selection techniques with decision support algorithms to identify key attributes and make accurate predictions regarding student performance. Through experimental evaluation, the system demonstrates its effectiveness in accurately assessing student performance based on selected features, with predictions closely aligning with actual observations. The results underscore the system's reliability and effectiveness in supporting informed decision-making processes, offering valuable insights for educators and administrators to enhance academic outcomes. The adaptability and versatility of the HFS-DSS make it well-suited for various educational contexts, providing potential applications in curriculum planning, student support services, and performance monitoring. The present paper highlights the significant contributions of the Integrated HFS-DSS towards enhancing decision-making processes and improving academic outcomes in educational institutions.

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Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model

Cited by 3 publications

References 23 publications

Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer

Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer

A cohort study of factors influencing the physical fitness of preschool children: a decision tree analysis

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

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