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Fuzzy Clustering Based Bayesian Framework to Predict Mental Health Problems among Children
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According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD), Pervasive Developmental Disorder(PDD), etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering.
The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.
Keywords
- Mental Health in India: A Wake-up Call, Available at: http://blogs.fortishealthcare.com/mental-health-india-wake-up-call/
- Bruce G. Buchanan and Edward H. Shortliffe, “Rule-based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project”, Addison-Wesley, 1984.
- Randolph A. Miller, Harry E. Pople and Jack D. Myers, “Internist-I, An Experimental Computer-based Diagnostic Consultant for General Internal Medicine”, New England Journal of Medicine, Vol. 307, pp. 468-476, 1982.
- R.H. Yap and D.M. Clarke, “An Expert System for Psychiatric Diagnosis using the DSM-III-R, DSM-IV and ICD-10 Classifications”, Proceedings of the AMIA Annual Fall Symposium, pp. 229-233, 1996.
- Kuryati Kipli, 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, Vol. 3, No. 5, pp. 444-448, 2013.
- D.H. Wolpert and W.G. Macready, “No Free Lunch Theorems for Search”, IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp. 67-82, 1997.
- Rozita Yati Masri and Hajar Mat Jani, “Employing Artificial Intelligence Techniques in Mental Health Diagnostic Expert System”, Proceedings of International Conference on Computer and Information Science, Vol. 1, pp. 495-499, 2012.
- David D. Luxton, “Artificial Intelligence in Psychological Practice: Current and Future Applications and Implications”, Professional Psychology: Research and Practice, Vol. 45, No. 5, pp. 332-339, 2014.
- D. Razzouk, J.J. Mari, I. Shirakawa, J. Wainer and D. Sigulem, “Decision Support System for the Diagnosis of Schizophrenia Disorders, Brazilian Journal of Medical and Biological Research, Vol. 39, No. 1, pp. 119-128, 2006.
- Subhagata Chattopadhyay, Preetisha Kaur, Fethi Rabhi and U. Rajendra Acharya, “An Automated System to Diagnose the Severity of Adult Depression”, Proceedings of 2nd International Conference on Emerging Applications of Information Technology, pp. 121-124, 2011.
- Luciano Comin Nunes, Placido Rogerio Pinheiro, Tarcisio Pequeno Cavalcante and Mirian Caliope Dantas Pinheiro, “Handling Diagnosis of Schizophrenia by a Hybrid Method”, Computational and Mathematical Methods in Medicine, Vol. 2015, pp. 1-13, 2015.
- S.R. Basavappa, S.L.Rao, B. Harish, Shantala R Basavappa and Shobini L Rao, “Expert System for Dementia/ Depression Diagnosis”, Nimhans Journal, Vol. 14, No. 2, pp. 99-106, 1996.
- Rashedur M. Rahman and Farhana Afroz, “Comparison of Various Classification Techniques using Different Data Mining Tools for Diabetes Diagnosis”, Journal of Software Engineering and Applications, Vol. 6, No. 3, pp. 85-97, 2013.
- David Gil and B. Mangus Johnson, “Diagnosing Parkinson by using Artificial Neural Networks and Support Vector Machines”, Global Journal of Computer Science and Technology, Vol. 9, No. 4, pp. 63-71, 2009.
- Jerzy Gomuła, Krzysztof Pancerz and Jarosław Szkoła, “Classification of MMPI Profiles of Patients with Mental Disorders-Experiments with Attribute Reduction and Extension”, Proceedings of International Conference on Rough Sets and Knowledge Technology, pp. 411-418, 2010.
- Flavio Luiz Seixas et.al., “A Bayesian Network Decision Model for Supporting the Diagnosis of Dementia, Alzheimer ׳s Disease and Mild Cognitive Impairment”, Computers in Biology and Medicine, Vol. 51, pp. 140-158, 2014.
- Anchana Khemphila and Veera Boonjing, “Parkinsons Disease Classification using Neural Network and Feature Selection”, World Academy of Science, Engineering and Technology, Vol. 6, No. 4, pp. 15-18, 2012.
- Mehdi Pirooznia et al., “Data Mining Approaches for Genome-Wide Association of Mood Disorders”, Psychiatric Genetics, Vol. 22, No. 2, pp. 55-61, 2012.
- Filip Dabek and Jesus J. Caban, “A Neural Network Based Model for Predicting Psychological Conditions”, Proceedings of International Conference on Brain Informatics and Health, pp. 252-261, 2015.
- Tawseef Ayoub Shaikh, “A Prototype of Parkinson’s and Primary Tumor Disease Prediction using Data Mining Techniques”, International Journal of Engineering Science Invention, Vol. 3, No. 4, pp. 23-28, 2014.
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