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The aim of this work is to develop a classification model to identify major depressive disorder (MDD) and attention deficit hyperactivity disorder (ADHD) in young teens and adults, which often go undetected. The model has trained and tested several classifiers, such as K-nearest neighbors (KNN), logistic regression, support vector machine (SVM), and a hybrid ensemble model of SVM, KNN, and logistic regression on TD-EEG Brain dataset and the results have been compared with various other existing to evaluate the effectiveness of the proposed model.

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Detection of Major Depressive Disorders in Children, Teens, and Young Adults

  • 会议论文

  • 发表日期：

    2025年

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  • Rushil Patra,
  • Ritej Dhamala,
  • Sadhana Tiwari,
  • Ritesh Chandra,
  • Sonali Agarwal

摘要

The aim of this work is to develop a classification model to identify major depressive disorder (MDD) and attention deficit hyperactivity disorder (ADHD) in young teens and adults, which often go undetected. The model has trained and tested several classifiers, such as K-nearest neighbors (KNN), logistic regression, support vector machine (SVM), and a hybrid ensemble model of SVM, KNN, and logistic regression on TD-EEG Brain dataset and the results have been compared with various other existing to evaluate the effectiveness of the proposed model.