Abstract
In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes is one of the most common chronic diseases which has affected to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can be aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with Type-2 diabetes mellitus. The processing and statistical analysis we used are Scikit-Learn, and Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge Type-2 diabetes mellitus chronic diseases.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
References
Arena, J.G.: Behavioral medicine consulation. In: Handbook of Clinical Interviewing with Adults, p. 446 (2007)
Mahmoodi,M., Hosseini-Zijoud, S.M., Hassan Shahi, G.H., Nabati, S., Modarresi, M., Mehrabian, M., Sayyadi, A.R., Hajizadeh, M.R.: J. Diabetes Endocrinol. 4(1), 1–5, January 2013. ISSN 2141-2685- Academic Journal
What is Diabetes? (n.d.). https://www.diabetesresearch.org/what-is-diabetes. Accessed 28 Aug 2017
The State of Diabetes in Bangladesh, 05 October 2016. http://futurestartup.com/2016/07/27/the-state-of-diabetes-in-bangladesh/. Accessed 28 Aug 2017
Vaz, N.C., Ferreira, A.M., Kulkarni, M.S., Vaz, F.S., Pintondian, N.R.: Prevalence of diabetic complications in rural Goa, India. J. Community Med. 36(4), 283–286 (2011). https://doi.org/10.4103/0970-0218.91330
Cao, H.B., Liu, P.A., Jiang, X.G., Jiang, Y.Y., Wang, J.P., Zheng, H., Zhang, H., Bennett, P.H., Howard, B.V.: Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and diabetes study. Diabetes Care 20, 537–544 (1997)
Nicole, R.: Title of paper with only first word capitalized. J. Name Stand. Abbrev. (in press)
Yorozu, Y., Hirano, M., Oka, K., Tagawa, Y.: Electron spectroscopy studies on magneto-optical media and plastic substrate interface. IEEE Transl. J. Magn. Japan 2, 740–741 (1987). [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982]
Rajesh, K., Sangeetha, V.: Application of data mining methods and techniques for diabetes diagnosis. Int. J. Eng. Innov. Technol. 2(3), 224–229 (2012)
Wang, C., Li, L., Wang, L., Ping, Z., Flory, M.T., Wang, G., Li, W.: Evaluating the risk of type 2 diabetes mellitus using artificial neural network: an effective classification approach. Diabetes Res. Clin. Pract. 100(1), 111–118 (2013)
Smith, A.E., Nugent, C.D., McClean, S.I.: Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example. Artif. Intell. Med. 27(1), 1–27 (2003)
Lin, C.S., Chiu, J.S., Hsieh, M.H., Mok, M.S., Li, Y.C., Chiu, H.W.: Predicting hypotensive episodes during spinal anesthesia with the application of artificial neural networks. Comput. Methods Programs Biomed. 92(2), 193–197 (2008)
Wolk, R., Berger, P., Lennon, R.J., Brilakis, E.S., Somers, V.K.: Body mass index. Circulation 108(18), 2206–2211 (2003)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2006)
Garreta, R., Moncecchi, G.: Learning Scikit-Learn: Machine Learning in Python. Packt Publishing Ltd., Birmingham (2013)
Hackeling, G.: Mastering Machine Learning with Scikit-Learn. Packt Publishing Ltd., Birmingham (2014)
Guo, C., Berkhahn, F.: Entity Embeddings of Categorical Variables. arXiv preprint arXiv:1604.06737 (2016)
Principe, J.C., Fancourt, C.L.: Artificial neural networks. In: Handbook of Global Optimization, vol. 2, pp. 363–386 (2013)
Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recognit. 36(2), 451–461 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Munna, M.T.A., Alam, M.M., Allayear, S.M., Sarker, K., Ara, S.J.F. (2019). Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-03402-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03401-6
Online ISBN: 978-3-030-03402-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)