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IJSTR >> Volume 8 - Issue 12, December 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



An Integrated Cognitive System (ICS) For Diabetes Mellitus

[Full Text]

 

AUTHOR(S)

Dr Vijay Franklin J, Kiruthikaa K V, Yuvaraj S, Ramya R

 

KEYWORDS

artificial intelligence, case based reasoning, cognitive system, diabetes mellitus, decision trees, intelligent system, machine learning, support vector machine

 

ABSTRACT

In the recent times, as per the health records of most of the countries in the globe, the Diabetes Mellitus (DM) are the most dreaded disease which heavily impacts the health of its victims. A wide range of artificial intelligence and machine learning techniques are utilized in health care domain for identifying and diagnosing diabetes mellitus disorders. The proposed system deals with an intelligent cognitive system for prognosis, diagnosis, treatment and behavioral analysis for drug pattern selection. It is an integrated system, means that the system is composed of identification, classification, prognosis, diagnosis, therapeutic plan, drug recommendation and disease eradication modules.

 

REFERENCES

[1] N. Barakat, A. P. Bradley and M. N. H. Barakat, "Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus," in IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 4, pp. 1114-1120, July 2010.
[2] Wheelock, K. M., Sinha, M., Knowler, W. C., Nelson, R. G., Fufaa, G. D., & Hanson, R. L., “Metabolic risk factors and type 2 diabetes incidence in American Indian children”, in the Journal of Clinical Endocrinology & Metabolism, 101(4), 1437- 1444, 2016.
[3] R. Priyadarshini, N. Dash and R. Mishra, "A Novel approach to predict diabetes mellitus using modified Extreme learning machine," 2014 International Conference on Electronics and Communication Systems (ICECS), Coimbatore, 2014, pp. 1-5.
[4] A. Swain, S. N. Mohanty and A. C. Das, "Comparative risk analysis on prediction of Diabetes Mellitus using machine learning approach," 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 2016, pp. 3312-3317.
[5] Zou Quan, Qu Kaiyang, Luo Yamei, Yin Dehui, Ju Ying, Tang Hua, “Predicting Diabetes Mellitus With Machine Learning Techniques”, in the journal of Frontiers in Genetics, Vol.no:9, 2018, pp.515.
[6] Hsin-Yi Tsao, Pei-Ying Chan and Emily Chia-Yu Su, “Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms”, in 13th International Symposium on Bioinformatics Research and Applications (ISBRA 2017), pp.111-121.
[7] Hosseini SM, Maracy MR, Amini M, Baradaran HR. A risk score development for diabetic retinopathy screening in Isfahan-Iran. J Res Med Sci. 2009;14(2):105–10.
[8] Semeraro F, Parrinello G, Cancarini A, Pasquini L, Zarra E, Cimino A, Cancarini G, Valentini U, Costagliola C “Predicting the risk of diabetic retinopathy in type 2 diabetic patients”, J Diabetes Complicat. 2011;25(5):292–7.
[9] Ogunyemi O, Kermah D. Machine learning approaches for detecting diabetic retinopathy from clinical and public health records. In: AMIA 2015 Annual Symposium Proceedings, American Medical Informatics Association; 2015, p.983–90.
[10] D. Nathani, M. H. Barnett, J. Spies, J. Pollard, and M. C. Kiernan, “Predicting a diagnosis of pathologically confirmed vasculitic neuropathy,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 89, no. 6, pp. A7.2–A7, May 2018.
[11] G. Jerums and R. J. MacIsaac, “Predicting renal function decline in patients with T2DM,” Nature Reviews Endocrinology, vol. 10, no. 7, pp. 381–382, May 2014.
[12] I. Blech, M. Katzenellenbogen, A. Katzenellenbogen, J. Wainstein, A. Rubinstein, I. Harman-Boehm, J. Cohen, T. I. Pollin, and B. Glaser, “Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model,” PLoS ONE, vol. 6, no. 4, p. e18743, Apr. 2011.
[13] M. Krochmal, G. Kontostathi, P. Magalhães, M. Makridakis, J. Klein, H. Husi, J. Leierer, G. Mayer, J.-L. Bascands, C. Denis, J. Zoidakis, P. Zürbig, C. Delles, J. P. Schanstra, H. Mischak, and A. Vlahou, “Urinary peptidomics analysis reveals proteases involved in diabetic nephropathy,” Scientific Reports, vol. 7, no. 1, Nov. 2017.
[14] W.-C. Lee, C.-Y. Fang, H.-C. Chen, S.-K. Hsueh, C.-J. Chen, C.-H. Yang, H.-K. Yip, C.-L. Hang, C.-J. Wu, and H.-Y. Fang, “Aspiration Thrombectomy and Drug-Eluting Stent Implantation Decrease the Occurrence of Angina Pectoris One Year After Acute Myocardial Infarction,” Medicine, vol. 95, no. 17, p. e3426, Apr. 2016.
[15] S. V. Arnold, J. A. Spertus, P. G. Jones, D. K. McGuire, K. J. Lipska, Y. Xu, J. M. Stolker, A. Goyal, and M. Kosiborod, “Predicting Adverse Outcomes After Myocardial Infarction Among Patients With Diabetes Mellitus,” Circulation: Cardiovascular Quality and Outcomes, vol. 9, no. 4, pp. 372–379, Jul. 2016.