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@article{185425,
        author = {Vaishnitha P and Uthika C and Trisha Visranthi S and Dr. G Vijaya},
        title = {Identification of children at risk of schizophrenia via Deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {1329-1335},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185425},
        abstract = {This study offers a novel approach for the early detection of children's schizophrenia risk that makes use of machine learning techniques, including Deep Learning, Voting Classifier, Naive Bayes, Random Forest, Decision Tree, and SVM. By using a comprehensive dataset that includes children's IDs, gender, age, migraine status, BPRS score, medication usage (including clozapine and traditional neuroleptics), and EEG data, the study aims to increase prediction accuracy. Through thorough analysis on a variety of sample populations, the study demonstrates the effectiveness of the proposed approach in accurately and consistently distinguishing between at-risk individuals. By offering targeted support and early intervention, this approach may enhance the timely identification and treatment of schizophrenia in children. For patients suffering from the illness, this could result in better long-term outcomes and a higher quality of life.},
        keywords = {Schizophrenia Risk Factors, Childhood Psychopathology, Electroencephalography (EEG)},
        month = {October},
        }