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@article{178055,
        author = {Arun A and Nithish Kumar W and Vidhya B},
        title = {Hybrid Method of Detecting Mental Health  Disorder Through Neural Networks & Random Forest},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8206-8212},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178055},
        abstract = {Mental health disorders pose a major global challenge, necessitating early detection for effective intervention. This approach proposes a hybrid classification framework integrating Random Forest and Neural Network (RF+NN) to enhance psychological disorder prediction using EEG data. Additionally, this technique aims to establish a foundation for personalized treatment strategies by identifying EEG-based biomarkers associated with specific mental health conditions. Evaluations on a dataset of 945 subjects with 1,148 EEG features demonstrate the hybrid model's superior performance, achieving 91.01% accuracy in binary classification (healthy vs. unhealthy) and 42.33% in multi-class classification, surpassing individual RF and NN models. The findings emphasize the advantages of hybrid classifiers in mental health assessment, particularly in feature selection and model interpretability, highlighting their potential for improving diagnostic accuracy.},
        keywords = {Mental health, EEG, Machine Learning, Neural Networks, Hybrid Models, Random Forests},
        month = {May},
        }