Abstract
The incidence of diabetes is on the rise all over the globe. Therefore, a proper approach is necessary to identify the diabetic patients at the earliest and provide appropriate lifestyle intervention in preventing or postponing the onset of diabetes. Hyperglycemia and hypoglycemia are two important consequences of diabetes computed on the basis of blood glucose level. In this paper, we propose a machine learning approach to identify the probability of occurrence of hyperglycemia with the impact of physical activity (exercise). This prediction will be helpful in order to reduce the risk factor of hyperglycemia by timely taken preventive step and changing their lifestyle.
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References
Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869–8879 (2017)
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., Chouvarda, I.: Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 15, 104–116 (2017)
Kaur, H., Kumari, V.: Predictive modelling and analytics for diabetes using a machine learning approach. Appl. Comput. Inform. (2018). https://doi.org/10.1016/j.aci.2018.12.004
Lin, X., et al.: Effects of exercise training on cardiorespiratory fitness and biomarkers of cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials. J. Am. Heart Assoc. 4(7), e002014 (2015)
Mapanga, R.F., Essop, M.F.: Damaging effects of hyperglycemia on cardiovascular function: spotlight on glucose metabolic pathways. Am. J. Physiol. Heart Circ. Physiol. 310(2), H153–H173 (2015)
Nathan, D.M., et al.: Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 32(1), 193–203 (2009)
Plis, K., Bunescu, R.C., Marling, C., Shubrook, J., Schwartz, F.: A machine learning approach to predicting blood glucose levels for diabetes management. In: AAAI Workshop: Modern Artificial Intelligence for Health Analytics, no. 31, pp. 35–39, March 2014
Rice, D., Kocurek, B., Snead, C.A.: Chronic disease management for diabetes: Baylor Health Care System’s coordinated efforts and the opening of the Diabetes Health and Wellness Institute. In: Baylor University Medical Center Proceedings, vol. 23, no. 3, pp. 230–234. Taylor & Francis, July 2010
Robertson, G., Lehmann, E.D., Sandham, W., Hamilton, D.: Blood glucose prediction using artificial neural networks trained with the AIDA diabetes simulator: a proof-of-concept pilot study. J. Electr. Comput. Eng. 2011, Article ID 681786, 11 p. (2011). https://doi.org/10.1155/2011/681786
Siddiqui, A.A., Siddiqui, S.A., Ahmad, S., Siddiqui, S., Ahsan, I., Sahu, K.: Diabetes: mechanism, pathophysiology and management-a review. Int. J. Drug Dev. Res. 5(2), 1–23 (2013)
Sokol-McKay, D.A.: What is Diabetes? http://www.visionaware.org/info/your-eye-condition/diabetic-retinopathy/what-is-diabetes/125. Accessed 25 Dec 2018
Sudharsan, B., Peeples, M., Shomali, M.: Hypoglycemia prediction using machine learning models for patients with type 2 diabetes. J. Diabetes Sci. Technol. 9(1), 86–90 (2015)
World Health Organization: Avoiding heart attacks and strokes: don’t be a victim-protect yourself. World Health Organization (2005)
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Agrawal, V., Singh, P., Sneha, S. (2019). Hyperglycemia Prediction Using Machine Learning: A Probabilistic Approach. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_29
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DOI: https://doi.org/10.1007/978-981-13-9942-8_29
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