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<p>This paper utilizes the data mining decision tree ID3 algorithm to improve the traditional ID3 algorithm, exclude the influence of other factors, and realize the decision tree ID3 algorithm by using the data mining decision tree ID3 algorithm in the database of college students’ mental health assessment in the application of mental health assessment in colleges and universities as an example. Among them, the number of nodes, the number of rules, the classification accuracy and the time of constructing the decision tree of the algorithm are compared to verify the improvement effect of the ID3 algorithm. The target dataset consists of psychological assessment data of students, which includes their basic situation and nine-dimensional psychological symptoms. Analyze the recorded data of students’ mental health status, extract the information on personality, parental relationship, economic income, and psychological abnormality, set the decision tree analysis variables, assign the value of students’ health status, and derive the specific factors affecting students’ mental health by using the decision tree If-Then classification rules. The validation results show that the generated decision tree ID3 model cross-validation estimate is 0.261, the standard error is 0.016, and its obtained standard error is less than 0.018, which indicates that the model fits better.</p>
</abstract>

This paper utilizes the data mining decision tree ID3 algorithm to improve the traditional ID3 algorithm, exclude the influence of other factors, and realize the decision tree ID3 algorithm by using the data mining decision tree ID3 algorithm in the database of college students’ mental health assessment in the application of mental health assessment in colleges and universities as an example. Among them, the number of nodes, the number of rules, the classification accuracy and the time of constructing the decision tree of the algorithm are compared to verify the improvement effect of the ID3 algorithm. The target dataset consists of psychological assessment data of students, which includes their basic situation and nine-dimensional psychological symptoms. Analyze the recorded data of students’ mental health status, extract the information on personality, parental relationship, economic income, and psychological abnormality, set the decision tree analysis variables, assign the value of students’ health status, and derive the specific factors affecting students’ mental health by using the decision tree If-Then classification rules. The validation results show that the generated decision tree ID3 model cross-validation estimate is 0.261, the standard error is 0.016, and its obtained standard error is less than 0.018, which indicates that the model fits better.

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper utilizes the data mining decision tree ID3 algorithm to improve the traditional ID3 algorithm, exclude the influence of other factors, and realize...

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

Data publikacji: 06 gru 2023

Zakres stron: -

Otrzymano: 25 lut 2023

Przyjęty: 16 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01377

Słowa kluczowe
Decision tree, ID3 algorithm, Data mining, Number of nodes, Student mental health

© 2023 Hongfeng Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

Data publikacji: 06 gru 2023

Zakres stron: -

Otrzymano: 25 lut 2023

Przyjęty: 16 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01377

Słowa kluczoweDecision tree, ID3 algorithm, Data mining, Number of nodes, Student mental health

© 2023 Hongfeng Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Decision tree, ID3 algorithm, Data mining, Number of nodes, Student mental health