Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model
Abstract The paper aims to construct an efficient predictive model to accurately predict information anxiety among college students and provides a scientific basis for mental health interventions. Firstly, the random forest algorithm is used to preprocess the relevant data and select the best featur...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00328-3 |
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| author | Bin Wang Li Shao |
| author_facet | Bin Wang Li Shao |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | Abstract The paper aims to construct an efficient predictive model to accurately predict information anxiety among college students and provides a scientific basis for mental health interventions. Firstly, the random forest algorithm is used to preprocess the relevant data and select the best features, and then the prediction model is built based on the CatBoost algorithm. To solve the problem of the imbalance of the dataset, a few class oversampling techniques are introduced, and the parameters of the model are optimized. The results showed that the model integrating random forest and category boosting algorithm had the lowest mean absolute error and root mean squared error, which were 0.125 and 0.142. Meanwhile, the area value under the receiver operating characteristic curve was close to 1, demonstrating excellent classification performance. The fusion model achieved an interpretable variance value of 0.923, an R2 value of 0.918, and a Logloss of 0.082, confirming its advantages in prediction accuracy and model fit. The fusion model had higher accuracy and stability in predicting information anxiety psychology among college students. This model can provide effective decision support for mental health educators, assist in early identification and intervention of information anxiety, and promote the mental health development of college students. |
| format | Article |
| id | doaj-art-b8d9196cd2184798963e514a02d8cda8 |
| institution | OA Journals |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-b8d9196cd2184798963e514a02d8cda82025-08-20T01:59:57ZengSpringerDiscover Artificial Intelligence2731-08092025-05-015111410.1007/s44163-025-00328-3Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion modelBin Wang0Li Shao1School of Social Sciences, Harbin Institute of TechnologySchool of Social Sciences, Harbin Institute of TechnologyAbstract The paper aims to construct an efficient predictive model to accurately predict information anxiety among college students and provides a scientific basis for mental health interventions. Firstly, the random forest algorithm is used to preprocess the relevant data and select the best features, and then the prediction model is built based on the CatBoost algorithm. To solve the problem of the imbalance of the dataset, a few class oversampling techniques are introduced, and the parameters of the model are optimized. The results showed that the model integrating random forest and category boosting algorithm had the lowest mean absolute error and root mean squared error, which were 0.125 and 0.142. Meanwhile, the area value under the receiver operating characteristic curve was close to 1, demonstrating excellent classification performance. The fusion model achieved an interpretable variance value of 0.923, an R2 value of 0.918, and a Logloss of 0.082, confirming its advantages in prediction accuracy and model fit. The fusion model had higher accuracy and stability in predicting information anxiety psychology among college students. This model can provide effective decision support for mental health educators, assist in early identification and intervention of information anxiety, and promote the mental health development of college students.https://doi.org/10.1007/s44163-025-00328-3Random forestCatBoostCollege studentInformation anxietyPsychological predictive analysis |
| spellingShingle | Bin Wang Li Shao Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model Discover Artificial Intelligence Random forest CatBoost College student Information anxiety Psychological predictive analysis |
| title | Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model |
| title_full | Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model |
| title_fullStr | Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model |
| title_full_unstemmed | Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model |
| title_short | Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model |
| title_sort | accurate prediction of college students information anxiety based on optimized random forest and category boosting fusion model |
| topic | Random forest CatBoost College student Information anxiety Psychological predictive analysis |
| url | https://doi.org/10.1007/s44163-025-00328-3 |
| work_keys_str_mv | AT binwang accuratepredictionofcollegestudentsinformationanxietybasedonoptimizedrandomforestandcategoryboostingfusionmodel AT lishao accuratepredictionofcollegestudentsinformationanxietybasedonoptimizedrandomforestandcategoryboostingfusionmodel |