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Machine learning-based prediction and classification of psychiatric symptoms induced by drug and plants toxicity

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  • Abdel Wahed, Salma
  • Abdel Wahed, Mutaz

Abstract

Psychiatric disorders induced by drug and plant toxicity represent a complex and underexplored area in medical research. Exposure to substances such as pharmaceuticals, illicit drugs, and environmental toxins can trigger a wide range of neuropsychiatric symptoms. This study proposes the development of a machine learning (ML) model to predict and classify these symptoms by analyzing open-access, de-identified datasets. Supervised and unsupervised learning techniques, including neural networks and algorithms like XGBoost, were applied to distinguish drug-induced psychiatric conditions from primary psychiatric disorders. The models were evaluated using metrics such as accuracy, precision, recall, and AUC-ROC. The XGBoost model demonstrated the best performance, achieving an AUC-ROC of 94.8%, making it a promising tool for clinical decision-support systems. This approach can improve early detection and intervention for psychiatric symptoms associated with drug toxicity, contributing to safer and more personalized healthcare.

Suggested Citation

  • Abdel Wahed, Salma & Abdel Wahed, Mutaz, 2025. "Machine learning-based prediction and classification of psychiatric symptoms induced by drug and plants toxicity," SAP Gamification and Augmented Reality, South American Publishing.
    • Handle: RePEc:cwf:grarti:gr2025107
    DOI: 10.56294/gr2025107
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    References listed on IDEAS

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    1. Mutaz Abdel Wahed & Muhyeeddin Alqaraleh & Mowafaq Salem Alzboon & Mohammad Subhi Al-Batah, 2025. "Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends," LatIA, AG Editor, vol. 3, pages 117-117.
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    Cited by:

    1. Abdel Wahed, Mutaz, 2025. "AI-Enhanced Threat Intelligence for Proactive Zero-Day Attack Detection," SAP Gamification and Augmented Reality, South American Publishing.

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