CFP last date
20 December 2024
Reseach Article

Implementation of Decision Tree Technique in the Diagnosis of Psychiatric Disorder

by Preeti Singh, Atma Prakash Singh, Shafeeq Ahmad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 8
Year of Publication: 2016
Authors: Preeti Singh, Atma Prakash Singh, Shafeeq Ahmad
10.5120/ijca2016910242

Preeti Singh, Atma Prakash Singh, Shafeeq Ahmad . Implementation of Decision Tree Technique in the Diagnosis of Psychiatric Disorder. International Journal of Computer Applications. 143, 8 ( Jun 2016), 28-31. DOI=10.5120/ijca2016910242

@article{ 10.5120/ijca2016910242,
author = { Preeti Singh, Atma Prakash Singh, Shafeeq Ahmad },
title = { Implementation of Decision Tree Technique in the Diagnosis of Psychiatric Disorder },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 8 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number8/25099-2016910242/ },
doi = { 10.5120/ijca2016910242 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:45:49.784529+05:30
%A Preeti Singh
%A Atma Prakash Singh
%A Shafeeq Ahmad
%T Implementation of Decision Tree Technique in the Diagnosis of Psychiatric Disorder
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 8
%P 28-31
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this work, the Electro-encephalogram and Magnetic Resonance Imaging (MRI) features along with physical, cognitive and psychological features combined together to diagnose the psychiatric disorder. The disorders are taken for diagnoses are Hyperactivity Disorder (HD), Memory Disorder (MD), Anxiety Disorder (AD), Obsession Disorder (OD) and Alzheimer (AZ). The diagnosis procedure of the disorder depends upon the different types of features. In this research paper we are considering 20 factors divided into five categories and with the help of decision tree based C5.0 method to know the important factors in the diagnosis of disorder. The design of decision tree for C5.0 method in consideration to Clementine tool is also matched with the manual calculation. The decision tree model supports doctor’s to get easier way to interpret and diagnoses disorder on the basis of important factors.

References
  1. Brown, G.W. and Harris, T. eds., 2012. Social origins of depression: A study of psychiatric disorder in women. Routledge.
  2. Swanson, J.W., Holzer III, C.E., Ganju, V.K. and Jono, R.T., 1990. Violence and psychiatric disorder in the community: evidence from the Epidemiologic Catchment Area surveys. Psychiatric Services, 41(7), pp.761-770.
  3. Fergusson, D.M., Horwood, L.J. and Lynskey, M.T., 1996. Childhood sexual abuse and psychiatric disorder in young adulthood: II. Psychiatric outcomes of childhood sexual abuse. Journal of the American Academy of Child & Adolescent Psychiatry, 35(10), pp.1365-1374.
  4. Ford, T., Vostanis, P., Meltzer, H. and Goodman, R., 2007. Psychiatric disorder among British children looked after by local authorities: comparison with children living in private households. The British Journal of Psychiatry,190(4), pp.319-325.
  5. Mining, D., 2001. Concepts and Techniques. Jiawei Han and Micheline Kamber.
  6. Strub, R.L. and Black, F.W., 1993. The mental status examination in neurology. FA Davis Company.
  7. Haerer, A.F., 1992. DeJong's the neurologic examination (No. 1992). Lippincott Williams & Wilkins.
  8. Strub, R.L. and Black, F.W., 1993. The mental status examination in neurology. FA Davis Company.
  9. Trzepacz, P.T. and Baker, R.W., 1993. The psychiatric mental status examination. Oxford University Press.
  10. Weintraub, S., 1985. Mental state assessment of young and elderly adults in behavioral neurology. Principles of behavioral neurology, pp.71-123.
  11. Exarchos, T.P., Tzallas, A.T., Fotiadis, D.I., Konitsiotis, S. and Giannopoulos, S., 2006. EEG transient event detection and classification using association rules. Information Technology in Biomedicine, IEEE Transactions on, 10(3), pp.451-457.
  12. Herrmann, C.S. and Demiralp, T., 2005. Human EEG gamma oscillations in neuropsychiatric disorders. Clinical Neurophysiology, 116(12), pp.2719-2733.
  13. Gangwar, M., Mishra, R.B. and Yadav, R.S., 2014. Classical and intelligent computing methods in psychiatry and neuropsychitry: an overview.International Journal of Advanced Research in IT and Engineering, 3(12), pp.1-24.
  14. Gangwar, M., Yadav, R.S. and Mishra, R.B., 2012, March. Semantic Web Services for medical health planning. In Recent Advances in Information Technology (RAIT), 2012 1st International Conference on (pp. 614-618). IEEE.
  15. Gangwar, M., Mishra, R.B. and Yadav, R.S., 2013. Intelligent Computing Methods for The Interpretation of Neuropsychiatric Diseases Based on Rbr-Cbr-Ann Integration. International Journal Of Computers & Technology, 11(5), pp.2490-2511.
  16. Servan-Schreiber, D., 1986. Artificial intelligence and psychiatry. The Journal of nervous and mental disease, 174(4), pp.191-202.
  17. Montague, P.R., Dolan, R.J., Friston, K.J. and Dayan, P., 2012. Erratum: Computational psychiatry.[Trends in Cognitive Sciences 16 (2012), 72-80](DOI: 10.1016/j. tics. 2011.11. 018). Trends in Cognitive Sciences.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Decision Tree Medical Computing Psychiatric Disorder Computational Intelligence.