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Journal of Intelligent Computing

An AlzheimerÃ¢â‚¬â„¢s Disease Prediction System using Fuzzy Logic

Gayathri, D. S, Nagarajan, M
Computer Science Department, Bharathiyar University Coimbatore, Tamil Nadu, India & Department of Computer Science CMS College of Science & Commerce, Coimbatore, Tamil Nadu, India

Abstract: Information Technology has modernized the approaches of the investigation done by the medical researchers as well as the experts in the clinical practice. Recent medical tools allow recording of massive volumes of patient data, which paves the way to know about the disease thoroughly and it helps for better healthcare measurements. But the medical experts in the clinical practice are unable to get benefit from the massive volumes of patient data. Thus there is a need for the computer based techniques, which supports the medical experts while making the diagnostic decisions. Thus the decision support system provides an intelligent report about the patient status based on the symptoms, lab reports and patient related information. Such Decision Support system helps to enhance the patient healthcare measures automatic based on knowledge discovery. Thus, this paper developed a software tool to validate the clinical data by implementing Fuzzy Logic method and data visualization methods for estimating the stages of the patient affected by the AlzheimerÃ¢â‚¬â„¢s disease. Here SAGE Test and ELISA test reports were used as biomarkers to analyze the stages of the AlzheimerÃ¢â‚¬â„¢s disease.

Keywords: AlzheimerÃ¢â‚¬â„¢s Disease, ELISA, SAGE, Fuzzy Logic, Dementia An AlzheimerÃ¢â‚¬â„¢s Disease Prediction System using Fuzzy Logic

DOI:https://doi.org/10.6025/jic/2019/10/4/137-149

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References:

1] Kloppel, S., Stonnington, C. M., Barnes, J., Chen, F., Chu, C., Good, C. D., Mader, I., Mitchell, L. A., Patel, A. C., Roberts, C. C. (2008). Accuracy of dementia diagnosis – a direct comparison between radiologists and a computerized method. Brain 131, 2969-2974.
[2] Huang, Q. R., Qin, Z., Zhang, S., Chow, C. M. (2008). Clinical patterns of obstructive sleep apnea and its comorbid conditions: A data mining approach. Journal of Clinical Sleep Medicine, 4, 543-550.
[3] Aigner, W., Kaiser, K., Miksch, S. (2008) Visualization Methods to Support Guideline-Based Care Management. Studies in Health Technology and Informatics, 139, 140-159.
[4] Bade, R., Schlechtweg, S., Miksch, S. (2004). Connecting time oriented data and information to a coherent interactive visualization. Proc CHI 2004, 105-112.
[5] Wang, T. D., Plaisant, C., Shneiderman, B. (2010). Visual Information Seeking in Multiple Electronic Health Records: Design Recommendations and a Process Model. Proc IHI 2010, 46-55.
[6] Hughes, L., Mthembu, M., Adams, L. (2011). Diagnostic work-up and treatment of dementia. Geriatric Medicine, 41(11) 595– 600.
[7] Wimo, A., Prince, M. J. (2010). World Alzheimer Report 2010: the global economic impact of dementia. London, UK: Alzheimer’s Disease International.
[8] Jack Jr, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., Lesnick, T. G., Pankratz, V. S., Donohue, M. C., Trojanowski, J. Q. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology, 12 (2) 207–216.
[9] Braak, H., Braak, E. (2012). Evolution of the neuropathology of Alzheimer’s disease. Acta Neurologica Scandinavica, 94 (165) 3–12.
[10] Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., Park, D. C., Reiman, E. M., Rowe, C. C., Siemers, E., Stern, Y., Yaffe, K., Carrillo, M. C., Thies, B., Morrison-Bogorad, M., Wagster, M. V., Phelps, C. H. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the
National Institute on Aging Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s and Dementia, 7 (3) 280–292.
[11] Osborn, G. G., Saunders, A. V. (2010). Current treatments for patients with Alzheimer disease. Journal of the American Osteopathic Association, 110 (9) 16–26.
[12] Jack Jr, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., Trojanowski, J. Q. (2010).
Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. The Lancet Neurology, 9 (1) 119.
[13] Sanchez, E., Toro, C., Artetxe, A., Graña, M., Sanin, C., Szczerbicki, E., Carrasco, E., Guijarro, F. (2013). Bridging challenges of Clinical Decision Support Systems with a semantic approach: a case study on breast cancer. Pattern Recognition Letters, Online publication ahead of print, doi: 10.1016/j.bbr.2011.03.031.
[14] Toro, C., Sanchez, E., Carrasco, E., Mancilla-Amaya, L., Sanín, C., Szczerbicki, E., Graña, M., Bonachela, P., Parra, C., Bueno, G., Guijarro, F. (2012). Using set of experience knowledge structure to extend a rule set of clinical decision support system for Alzheimer’s disease diagnosis. Cybernetics and Systems, 43(2) 81–95.
[15] Jost, B. C., Grossberg, G. T. (1996), The Evolution of Psychiatric Symptoms in Alzheimer’s Disease: A Natural History Study. Journal of the American Geriatrics Society, 44, 1078–1081. doi:10.1111/j.1532-5415.1996.tb02942.x.
[16] Article: Basics of Alzheimer’s Disease, published by, alzheimer’s association, Rev. Oct16 770-10-0003.
[17] Scharre, Douglas, W. (2010). Self-administered Gerocognitive Examination (SAGE): a brief cognitive assessment Instrument for mild cognitive impairment (MCI) and early dementia. Alzheimer Disease & Associated Disorders 24 (1) 64-71.
[18] Lopez, Oscar, L. (2017). Mild Cognitive Impairment. Continuum/: Lifelong Learning in Neurology 19.2 Dementia (2013), 411–424. PMC. Web. 19 Apr. 2017.
[19] Li-San Wang. (2013). Comparison of xMAP and ELISA assays for detecting CSF biomarkers of Alzheimer’s Disease, Alzheimers Dis. Author manuscript; available in PMC 2013 May 22.

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