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A Random Forest Model for Mental Disorders Diagnostic Systems

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Book cover Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

Data mining has established new applications in medicine over the last few years. Using mental disorders diagnostic systems, data possession, and data analysis has been of enormous succor for clinicians to recognize diseases more precisely, especially when dealing with overlapping mental symptoms. In this study, random forests used to predict a number of mental disorders and drug abuse. Two datasets were used, the first dataset consists of substances abuse patients derived from United States, and the second dataset consists of psychotic patients collected from Egypt. Random Forest classification technique was used to increase the accuracy rate of mental disorders prediction systems. The exploratory data mining analysis produced two models of the random forest algorithm with two groups of patients at different risks for substances abuse and psychotic diseases. This study presents a new data mining approach to enhance the diagnosis of mental disorders. This approach also serves therapeutic interventions.

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Notes

  1. 1.

    Trial Registration—Psychotic diseases dataset used in this study came from isrctn.com, identifier: ISRCTN75534069.

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Author information

Authors and Affiliations

  1. College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport, P.O. Box 1029, Alexandria, Egypt

    Horeya Abou-Warda, Nahla A. Belal & Yasser El-Sonbaty

  2. Department of Dual Diagnosis, El-Mamoura Hospital for Psychiatric and Addiction Treatment, Alexandria, Egypt

    Sherif Darwish

Authors
  1. Horeya Abou-Warda
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  2. Nahla A. Belal
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  3. Yasser El-Sonbaty
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  4. Sherif Darwish
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Corresponding author

Correspondence to Nahla A. Belal .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Abou-Warda, H., Belal, N.A., El-Sonbaty, Y., Darwish, S. (2017). A Random Forest Model for Mental Disorders Diagnostic Systems. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_64

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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