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Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.

Susel Góngora Alonso1, Isabel de la Torre-Díez2, Sofiane Hamrioui3

  • 1Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.

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Data Mining techniques enhance understanding and classification of mental health conditions like Alzheimer's and depression. This review highlights their role in improving clinical decisions and patient outcomes.

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Area of Science:

  • Medical Informatics
  • Computational Psychiatry
  • Data Science

Background:

  • Data Mining is crucial for advancing medical prognosis and disease classification, particularly in mental health.
  • Prevalent mental health conditions like Dementia, Alzheimer's, Schizophrenia, and Depression require innovative analytical approaches.

Purpose of the Study:

  • To review existing research on Data Mining techniques and algorithms applied to major mental health diseases.
  • To identify trends and applications of Data Mining in understanding Dementia, Alzheimer's, Schizophrenia, and Depression.

Main Methods:

  • Systematic literature review of academic databases (Google Scholar, PubMed, Scopus, etc.) from 2008 to present.
  • Search criteria included 'Data Mining', 'Mental Health', specific disease names, and 'techniques'/'algorithms'.
  • Analysis of 211 articles, with 72 identified as relevant works.

Main Results:

  • Data Mining applications are prevalent in Alzheimer's (32%), Depression (24%), Dementia (22%), Schizophrenia (14%), and Bipolar Disorders (8%).
  • A significant portion of reviewed studies focuses on predicting risk factors for these mental health conditions.
  • The application of Data Mining shows promise in aiding clinical decision-making and diagnosis.

Conclusions:

  • Data Mining techniques offer substantial benefits for the clinical decision-making process in mental health.
  • Predictive diagnosis and improved patient quality of life are key outcomes facilitated by Data Mining in mental health.
  • Further research integrating Data Mining into mental healthcare is warranted to leverage its full potential.