Data Mining Based Predictive Analysis Of Diabetic Diagnosis In Health Care: Overview
Keywords:
Machine Learning, Data Mining, Diabetes Mellitus (DM), Diabetic complications, Disease prediction modelsAbstract
The complexity of modernizing the healthcare industry's journey toward processing huge health data and accessing them for analysis and action will be considerably increased. Health research breakthroughs have resulted in a substantial quantity of data being generated from enormous electronic health records, high-throughput genomic data, for example as well as the collection of clinical information. The healthcare business confronts several obstacles, emphasizing the significance of data analytics development. In biosciences, machine learning and data mining methods are becoming more crucial in attempts to effectively convert all available data into valuable information. This is the goal of these approaches. mining techniques in today's medical studies. An overview is the goal of this work. The condition known as Diabetic Mellitus affects millions of individuals worldwide (DM). There were a lot of clinical datasets that were utilised. New hypotheses targeted at greater understanding and future study in DM may be derived from the title applications in the chosen works. According to the findings, this strategy is an effective method to treat and care for patients while also improving outcomes like as cost and availability.
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References
Han and Kamber, “Data Mining:Conceots and Technique”, margen Kaufmann Publisher,2006-Elsevier inc.
Pawan Kumar Tiwari, Mukesh Kumar Yadav, R. K. G. A. . (2022). Design Simulation and Review of Solar PV Power Forecasting Using Computing Techniques. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(5), 18–27. https://doi.org/10.17762/ijrmee.v9i5.370
Asao K, Sarti C, Forsen T et al., “Long-term mortality in nation wide cohorts of childhood-onset type 1 diabetes in Japan and Finland". Diabetes Care 26:2037–2042, 2003.
HumarKahramanli and NovruzAllahverdi, “Design of a Hybrid System for the Diabetes and Heart Disease”, Expert Systems with Applications: An International Journal, Volume 35 Issue 1-2, July, 2008.
Kaul, K., Tarr, J. M., Ahmad, S. I., Kohner, E. M., &Chibber, R. “Introduction to diabetes mellitus. In Diabetes” (pp. 1-11). Springer New York, 2013.
American Diabetes Association. "Diagnosis and classification of diabetes mellitus." Diabetes care 31.Supplement 1: S55-S60, 2008.
Gill, D. R. . (2022). A Study of Framework of Behavioural Driven Development: Methodologies, Advantages, and Challenges. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 09–12. https://doi.org/10.17762/ijfrcsce.v8i2.2068
Namayanja, J., &Janeja, V. P., “An assessment of patient behavior over time periods: A case study of managing type 2 diabetes through blood glucose readings and insulin doses”. Journal of Medical Systems, 2012.
Aljumah, A. A., Siddiqui, M. K., & Ahamad, M. G. “Application of classification based data mining technique in diabetes care”. Journal of Applied Sciences, 13(3), 416-422, 2013.
Miroslav Marinov, M.S.et.al., “Data-Mining Technologies for Diabetes: A Systematic Review” Journal of Diabetes Science and Technology, Volume 5, Issue 6, November 2011.
Singh, S. . (2022). Unconditionally G ?odel Degeneracy for Quasi-Meager, Smooth Moduli. International Journal on Recent Trends in Life Science and Mathematics, 9(1), 28–36. https://doi.org/10.17762/ijlsm.v9i1.139
K.Srinivas, Dr.G.Raghavendra Rao , Dr.A.Govardhan, “Analysis of Coronary Heart Disease and Prediction of Heart Attack in coal mining regions using data mining techniques”, The 5th International Conference on Computer Science & Education Hefei, China., p(1344 - 1349). August 24–27, 2010.
Kabisha, M. S., Rahim, K. A., Khaliluzzaman, M., & Khan, S. I. (2022). Face and Hand Gesture Recognition Based Person Identification System using Convolutional Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 105–115. https://doi.org/10.18201/ijisae.2022.273
Jyoti Soni.et.al., ” Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction”, International Journal of Computer Applications (0975 – 8887) Volume 17– No.8, March 2011.
Krishnaveni, S. ., A. . Lakkireddy, S. . Vasavi, and A. . Gokhale. “Multi-Objective Virtual Machine Placement Using Order Exchange and Migration Ant Colony System Algorithm”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 6, June 2022, pp. 01-09, doi:10.17762/ijritcc.v10i6.5618.
Chauraisa V., and Pal, S.,”Data Mining Approach to Detect Heart Diseases”, International Journal of Advanced Computer Science and Information Technology (IJACSIT), 2, (4), pp 56-66, 2013.
Srinivas, K., “Analysis of Coronary Heart Disease and Prediction of Heart Attack in coal mining regions using data mining techniques”, IEEE Transaction on Computer Science and Education (ICCSE), p(1344 - 1349), 2010.
Sally Fouad Shady. (2021). Approaches to Teaching a Biomaterials Laboratory Course Online. Journal of Online Engineering Education, 12(1), 01–05. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/43
“American Diabetes Association. Screening for type 2 diabetes”. Diabetes Care, 27:S11– 4, 2004.
Zimmet P, Shaw J, Alberti KG, “Preventing type 2 diabetes and the metabolic syndrome in the real world: a realistic view”. Diabetic Med 2003;20:693–702.
Ferchak V, Meneghini LF. Obesity, bariatric surgery and type 2 Diabetes: a systematic review. Diabetes Metab Res Rev 2004;20:438 – 45.
Anusha, D. J. ., R. . Anandan, and P. V. . Krishna. “Modified Context Aware Middleware Architecture for Precision Agriculture”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 7, July 2022, pp. 112-20, doi:10.17762/ijritcc.v10i7.5635.
Retnakaran R, Cull CA, Thorne KI, Adler AI, Holman RR, “Risk factors for renal dysfunction in type 2 diabetes”, U.K. Prospective Diabetes Study 74. Diabetes 55:1832–1839, 2006.
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