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Currently, anxiety disorders are one of the most common mental health conditions worldwide, and they have brought serious consequences because they were not treated in time because it was not known that the patient suffered from such a condition. Therefore, the objective of this study is to propose a classification model that uses ML techniques to detect the type of anxiety. The applied methodology consisted of 5 phases: Data-set acquisition, Preprocessing, Feature Selection (LightGBM, RF, Gradient-Boosting, Linear SVM, AdaBoost, Logistic Regression), Machine Learning (LR, RF, XGBoost, NB, SVM, MLP). The best results were obtained for the model using the 13 most important features according to Gradient Boosting, together with the XGBoost classification algorithm, with results after tuning with GridSearch (colsample_bytree: 0. 6; learning_rate: 0.2; max_depth: 5; min_child_weight: 1; n_estimators: 150; subsample: 0.6) of Accuracy, 96.88%, Precision, 96.89%, Recall, 96.88%; and F1-Score, 96.88%. The results succeeded in proposing a robust classification model for the classification of anxiety types.

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Proposed Anxiety Classification Model Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Diego Fiestas-Arroyo,
  • Wilfredo Ticona

摘要

Currently, anxiety disorders are one of the most common mental health conditions worldwide, and they have brought serious consequences because they were not treated in time because it was not known that the patient suffered from such a condition. Therefore, the objective of this study is to propose a classification model that uses ML techniques to detect the type of anxiety. The applied methodology consisted of 5 phases: Data-set acquisition, Preprocessing, Feature Selection (LightGBM, RF, Gradient-Boosting, Linear SVM, AdaBoost, Logistic Regression), Machine Learning (LR, RF, XGBoost, NB, SVM, MLP). The best results were obtained for the model using the 13 most important features according to Gradient Boosting, together with the XGBoost classification algorithm, with results after tuning with GridSearch (colsample_bytree: 0. 6; learning_rate: 0.2; max_depth: 5; min_child_weight: 1; n_estimators: 150; subsample: 0.6) of Accuracy, 96.88%, Precision, 96.89%, Recall, 96.88%; and F1-Score, 96.88%. The results succeeded in proposing a robust classification model for the classification of anxiety types.