Abstract
A healthcare system using modern computing techniques is the highest explored area in healthcare research. Researchers in the field of computing and healthcare are persistently working together to make such systems more technology ready. Recent studies by World Health Organization have shown an increment in the number of diabetic patients and their deaths. Diabetes is one of the basic sicknesses which has long-haul complexities related to it. A high volume of medical information is produced. It is important to gather, store, learn and predict the health of such patients using continuous monitoring and technological innovations. An alarming increase in the number of diabetic patients in India has become an important area of concern. With the assistance of innovation, it is important to construct a framework that store and examine the diabetic information and further see conceivable dangers. Its early detection and analysis remain a challenge among researchers. This review gives present status of research in determining diabetes and proposed frameworks.
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References
Xu, W., Zhang, J., Zhang, Q., Wei, X.: Risk prediction of type II diabetes based on random forest model. In: Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). (2017). https://doi.org/10.1109/aeeicb.2017.7972337
Gandhi, K.K., Prajapati, N.B.: Diabetes prediction using feature selection and classification. Int. J. Adv. Eng. Res. Develop. (2014). https://doi.org/10.21090/ijaerd.0105110
Panwar, M., Acharyya, A., Shafik, R.A., Biswas, D.: K-nearest neighbor based methodology for accurate diagnosis of diabetes mellitus. In: Embedded Computing and System Design (ISED), pp. 132–136. IEEE (2016). https://doi.org/10.15417/1881
Sowjanya, K., Singhal, A., Choudhary, C.: MobDBTest: a machine learning based system for predicting diabetes risk using mobile devices. In: Advance Computing Conference (IACC), pp. 397–402. IEEE (2015). https://doi.org/10.1109/iadcc.2015.7154738
Heydari, M., Teimouri, M., Heshmati, Z., Alavinia, S.M.: Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. Int. J. Diabetes Develop. Countries, 167–173. (2016). https://doi.org/10.15417/1881
Komi, M., Li, J., Zhai, Y., Zhang, X.: Application of data mining methods in diabetes prediction. Image Vision Comput. (ICIVC) (2017). https://doi.org/10.1109/icivc.2017.7984706
Swain, A., Mohanty, S.N., Das A.C.: Comparative risk analysis on prediction of diabetes mellitus using machine learning approach. In: Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 3312–3317 (2016). https://doi.org/10.1109/iceeot.2016.7755319
Douali, N., Dollon, J., Jaulent, M.-C.: Personalized prediction of gestational diabetes using a clinical decision support system. In: Fuzzy Systems (FUZZ-IEEE), pp. 1–5 (2015). https://doi.org/10.1109/fuzz-ieee.2015.7337813
Bhatia, N., Kumar, S.: Prediction of severity of diabetes mellitus using fuzzy cognitive maps. Adv. Life Sci. Technol. 29, 71–78 (2015)
Samant, P., Agarwal, R.: Diagnosis of Diabetes using computer methods: soft computing methods for diabetes detection using iris (2017). https://doi.org/10.1016/j.cmpb.2018.01.004
Chang, S.H., Chiang, R.D., Wu, S.J., Chang, W.T.: A context-aware, interactive M-health system for diabetics. IT Prof. 18(3), 14–22 (2016)
Gomez, J., Oviedo, B., Zhuma, E.: Patient monitoring system based on internet of things. Proc. Comput. Sci. 83, 90–97 (2016). https://doi.org/10.1016/j.procs.2016.04.103
Gupta, P.K., Maharaj, B.T., Malekian, R.: A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres. Multimed. Tools Appl. 76(18), 18489–18512 (2017). https://doi.org/10.1007/s11042-016-4050
Antonovici, D.-A., et al.: Acquisition and management of biomedical data using Internet of Things concepts. In: International Symposium on Fundamentals of Electrical Engineering (ISFEE). IEEE (2014). https://doi.org/10.1109/isfee.2014.7050625
Al-Taee, M.A., Al-Nuaimy, W., Al-Ataby, A., Muhsin, Z.J., Abood, S.N.: Mobile health platform for diabetes management based on the Internet-of-Things. In: Applied Electrical Engineering and Computing Technologies (AEECT) (2015). https://doi.org/10.1109/aeect.2013.6716427
Natarajan, K., Prasad, B., Kokila, P.: Smart health care system using internet of things. J. Netw. Commun. Emerg. Technol. (JNCET) 6(3) (2016)
Rathore, M.M., Ahmad, A., Paul, A., Wan, J., Zhang, D.: Real-time medical emergency response system: exploiting IoT and big data for public health. J. Med. Syst. 40(12), 283 (2016)
Jara, A.J., Zamora, M.A., Skarmeta, A.F.: An internet of things—based personal device for diabetes therapy management in ambient assisted living (AAL). In: Personal and Ubiquitous Computing (2011). https://doi.org/10.1007/s00779-010-0353-1
Rahman, R.A., et al.: IoT-based personal health care monitoring device for diabetic patients. Comput. Appl. Industr. Electron. (ISCAIE) (2017). https://doi.org/10.1109/iscaie.2017.8074971
Bhat, G.M., Bhat, N.G.: A novel IoT based framework for blood glucose examination. In: Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 205–207 (2017). https://doi.org/10.1109/iceeccot.2017.8284666
Winterlich, A., et al.: Diabetes digital coach: developing an infrastructure for e-health self-management tools. In: Developments in Systems Engineering (DeSE), 9th International Conference (2016). https://doi.org/10.1109/dese.2016.56
Mall, S., Gupta, M., Chauhan, R.: Diet monitoring and management of diabetic patient using robot assistant based on Internet of Things. In: Emerging Trends in Computing and Communication Technologies (ICETCCT) (2017). https://doi.org/10.1109/icetcct.2017.8280339
Istepanian, R.S., Hu, S., Philip, N.Y., Sungoor, A.: The potential of Internet of m-health Things “m-IoT” for non-invasive glucose level sensing. In: Engineering in Medicine and Biology Society, EMBC, pp. 5264–5266, Aug 2011
Ephzibah, E.P.: Cost effective approach on feature selection using genetic algorithms and fuzzy logic for diabetes diagnosis. Int. J. Soft Comput. (IJSC) 2, 1–10 (2011). https://doi.org/10.5121/ijsc.2011.2101
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Sharma, N., Singh, A. (2019). Diabetes Detection and Prediction Using Machine Learning/IoT: A Survey. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_42
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