Data mining algorithms and its application having different and diverse field. One of the challenging and emerging research fields is medical science. Medical field exhibit many type of data and thus it provide large statically and dynamic data, its relevant Meta data which contains a detail information about the observation made in different scenario. Medical data such as EEG, ECG reports, MRI reports, different lab test reports and other observation which are either in text format or graphical image format derived. These are different sample format describe various symptoms and prevention associate with those symptoms. Genetic activity, genes presence and repetition also gives user activity detail and problems in human body. Mental illness is a type of disease which relate with the mental disorder. It deals in human interaction, its behavior, and its regular and irregular activity. In this stage human sudden start misbehave or extra activities during the particular phase and hence an un-predictable incident may occur. Psychometric disease cases are increasing day by day with different symptoms in human being. These are diseases which occur due to over work pressure, mental activity, some past human life incident or may be the victim of some good or bad incident. It leads to several problems in society, as per well issue with the person who is suffering with the particular disorder. An human interaction and understanding capacity is different than a normal person which keep person separate from the normal society and social life. These disorders also occur sometime very violent and make it very tedious to recover. Psychiatric relate to brain disorder, Mental disorder, irregular work performing in public etc. Diagnosis over the disease is complex and not fixed. In the previous research on the same concern shows that the past data can be analyzed properly and it can further be useful for the precautions. Mental disorder records may leads to several Meta data, report activities observed by hospital or supervision person. A classification over the given symptoms, its action and medical treatment help in diagnosis. Past studies always parts in important role using which further decision can be taken. Mental disorder, mental health, further possibilities can be observed and driven remedy can be discussed. Data classification algorithm makes it possible to classify the normal and abnormal data cases. Hospital release dataset give the information which tends to changes, mental health of previous patients and their best precautions taken to cure them. Different algorithm model is used which help in mining the previous algorithm data rule over the past cases. It enable user to understand past cases, their symptoms, disease occurred and its diagnosis steps taken. Various algorithms such as Genetic algorithm, Naïve bayes, Ant colony optimization, Rule based reasoning (RBR), Case based reasoning (CBR) and Artificial neural network (ANN) is used for psychometric disease finding from the large dataset. In this paper survey of past techniques is performed. This paper also illustrates the work finding and limitation of previous research. An analysis using the real time dataset over the proposed algorithm is left for further work..


Data mining, Psychiatric disorder, mental disorder diagnosis, classification technique, ANN approach.

Full Text:



. Mohit Gangwar, R. B. Mishra, R. S. Yadav, B. Pandey, “Intelligent Computing Methods for The Interpretation of Neuropsychiatric Diseases Based on Rbr-Cbr-Ann Integration”, INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY Vol 11, No. 5, 2013.

. American Psychiatric Association. "Diagnostic and statistical manual of mental disorders" (5th ed.). Arlington, VA: American Psychiatric Publishing, (2013).

. Piers Johnson , Luke Vandewater , William Wilson, Paul Maruff, Greg Savage , Petra Graham5 , Lance S Macaulay , Kathryn A Ellis, Cassandra Szoeke , Ralph N Martins, Christopher C Rowe, Colin L Masters, David Ames, Ping Zhang, “Genetic algorithm with logistic regression for prediction of progression to Alzheimer’s disease”, Johnson et al. BMC Bioinformatics 2014, 15(Suppl 16):S11.

. Francesca Pistollato, Elan L. Ohayon, Ann Lam,” Alzheimer disease research in the 21st century: past and current failures, new perspectives and funding priorities”, Oncotarget, Advance Publications 2016.

. Ms. Sumathi M.R, Dr. B. Poorna, “Prediction of Mental Health Problems Among Children Using Machine Learning Techniques”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 1, 2016.

. Richard G Jackson, Rashmi Patel, Nishamali Jayatilleke, Anna Kolliakou, Michael Ball, Genevieve Gorrell, Angus Roberts, Richard J Dobson, Robert Stewart,” Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project”, Jackson RG, et al. BMJ Open 2017;6:e012012. doi:10.1136/bmjopen-2016-012012.

. Yevgeniya Kovalchuk1 , Robert Stewart, Matthew Broadbent , Tim J. P. Hubbard, Richard J. B. Dobson,” Analysis of diagnoses extracted from electronic health records in a large mental health case register”, PLOS ONE | DOI:10.1371/journal.pone.0171526 February 16, 2017.

. Mohit Gangwar, R. B. Mishra, R. S. Yadav, B. Pandey, “Intelligent Computing Method for the Interpretation of Neuropsychiatric Diseases”, International Journal of Computer Applications (0975 – 8887) Volume 55– No.17, October 2012.

. Prentzas, Jim, and Ioannis Hatzilygeroudis. "Categorizing approaches combining rule‐based and case‐based reasoning." Expert Systems 24.2 (2007): 97-122.

. Kipli, Kuryati, Abbas Z. Kouzani, and Isredza Rahmi A. Hamid. "Investigating machine learning techniques for detection of depression using structural MRI volumetric features." International journal of bioscience, biochemistry and bioinformatics 3.5 (2013): 444-448

. Marling, Cindy, and Peter Whitehouse. "Case-based reasoning in the care of Alzheimer’s disease patients." Casebased reasoning research and development. Springer Berlin Heidelberg, 2001. 702-715.

. Sanei, Saeid, and Jonathon A. Chambers. "EEG signal processing." Wiley. com, 2008.

. Vollala, Satyanarayana, and Karnakar Gulla. "Automatic Detection of EPILEPSY EEG Using Neural Networks."

. Carrillo MC, Sanders CA and Katz RG. Maximizing the Alzheimer’s Disease Neuroimaging Initiative II. Alzheimer’s & dementia. 2009; 5:271-275.

. M. B. (2005). Clinical utility: A prerequisite for the adoption of a dimensional approach in DSM. Journal of Abnormal Psychology, 114, 560-564. doi:10.1037/0021-843X.114.4.560.

. Clarke, D. E., Narrow, W. E., Regier, D. A., Kuramoto, S. J., Kupfer, D. J., Kuhl, E. A., . . . Kraemer, H. C. (2013). DSM-5 field trials in the United States and Canada, Part I: Study design, sampling strategy, implementation, and analytic approaches. American Journal of Psychiatry, 170, 43-58. doi:10.1176/appi.ajp.2012.12070998.

. Krueger, R. F., & Bezdjian, S. (2009). Enhancing research and treatment of mental disorders with dimensional concepts: Toward DSM-V and ICD-11. World Psychiatry, 8, 3-6.

. Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., . . . Wang, P. (2010). Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167, 748-751. doi: 10.1176/appi.ajp.2010.09091379

. IBGE (2001) Censo demogra´fico populacional do ano 2000. Instituto Brasileiro de Geografia e Estatı´stica Website. Available: http://www.ibge.gov.br/censo/. Accessed 2011 Sept 10.

. Degenhardt L, Chiu WT, Sampson N, Kessler RC, Anthony JC, et al. (2008) Toward a global view of alcohol, tobacco, cannabis, and cocaine use: findings from the WHO World Mental Health Surveys. PLoS Med 5: e141.

. Hennigan T (2010) Economic success threatens aspirations of Brazil’s public health system. BMJ 341: c5453.

. Mello MF, Kohn R, Mari JdJ, Andrade LH, Almeida-Filho Nd, et al. (2009) The Epidemiology of Mental Disorders in Brazil [La Epidemiologı´a de las Enfermedades Mentales en Brasil]; Rodrı´guez J, Kohn R, Aguilar-Gaxiola S, eds. Washington, DC: Pan American Health Organization. pp 101–117.

. The World Bank Website. GINI Index.Available: http://data.worldbank.org/ indicator/SI.POV.GINI/. Accessed 2011 Sept 10.

. Peen J, Schoevers RA, Beekman AT, Dekker J (2010) The current status of urban-rural differences in psychiatric disorders. Acta Psychiatr Scand 121: 84–93.

. Cuthbert, B. N. (2014). The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry, 13, 28-35. doi:10.1002/wps.20087.

. American Psychiatric Association. (2015). Structured clinical interview for DSM-5 (SCID-5). Retrieved from http://www. appi.org/products/structured-clinical-interview-for-dsm- 5-scid-5.

. http://slideplayer.com/slide/9363646/

DOI: https://doi.org/10.26483/ijarcs.v8i8.4653


  • There are currently no refbacks.

Copyright (c) 2017 International Journal of Advanced Research in Computer Science