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UNVEILING THE CONNECTION BETWEEN PSYCHOACTIVE DRUG USE AND MENTAL HEALTH USING KDD PROCESS

  • Unique Paper ID: 167102
  • Volume: 11
  • Issue: 3
  • PageNo: 269-278
  • Abstract:
  • This study investigates the efficacy of machine learning (ML) techniques in predicting Common Mental Disorders (CMD) and depression among users of psychoactive substances. Utilizing a dataset comprising 605 samples from individuals in Ceara, Brazil, collected between January and July 2019, various ML algorithms were employed, including MLP, SVM, ELM, RF, KNN, QDA, Naive QDA, Naive LDA, LDA, and a Voting Classifier combining Bagging Classifier with RF and Decision Tree. The results demonstrated significant accuracy, with SVM achieving 82.81% for CMD prediction and 81.98% for depression, particularly when Sequential Backward Selection (SBS) feature selection was applied. As an extension, ensemble methods like Voting Classifier were explored, aiming for even higher accuracy, potentially exceeding 90%. Additionally, the implementation of a front end using the Flask framework for user testing, along with user authentication, was proposed to enhance usability and accessibility. This study contributes to the development of predictive models for mental health disorders among psychoactive drug users, facilitating early intervention and support.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 3
  • PageNo: 269-278

UNVEILING THE CONNECTION BETWEEN PSYCHOACTIVE DRUG USE AND MENTAL HEALTH USING KDD PROCESS

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Journal no 47859

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