Abstract
Around 400 million people suffer from diabetes around the world. Diabetes prediction is challenging as it involves complex interactions or interdependencies between various human organs like eye, kidney, heart, etc. The machine learning (ML) algorithms provide an efficient way of predicting the diabetes. The objective of this work is to build a system using ML techniques for the accurate forecast of diabetes in a patient. The decision tree (DT) algorithms are well suited for this. In this work, we have applied the DT algorithm to forecast type 2 diabetes mellitus (T2DM). Extensive experiments were performed on the Pima Indian Diabetes Dataset (PIDD) obtained from the UCI machine learning repository. Based on the results, we observed that the decision tree was able to forecast accurately when compared to the SVM algorithm on the diabetes data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, A.B.E.D., Elaraby, I.S.: Data mining: a prediction for student’s performance using classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)
Altaher, A., BaRukab, O.: Prediction of student’s academic performance based on adaptive neuro-fuzzy inference. Int. J. Comput. Sci. Netw. Secur. 17(1), 165 (2017)
Acharya, A., Sinha, D.: Early prediction of student performance using machine learning techniques. Int. J. Comput. Appl. 107(1), 37–43 (2014)
Kaur, P., Singh, M., Josan, G.S.: Classification and prediction based data mining algorithms to predict slow learners in education sector. In: 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015) (2015)
Guruler, H., Istanbullu, A., Karahasan, M.: A new student performance analysing system using knowledge discovery in higher educational databases. Comput. Educ. 55(1), 247–254 (2010). https://doi.org/10.1016/j.compedu.2010.01.010. ISSN 0360-1315
Vandamme, J.-P., Meskens, N., Superby, J.-F.: Predicting academic performance by data mining methods. Educ. Econ. 15(4), 405–419 (2007). https://doi.org/10.1080/09645290701409939
Abuteir, M., El-Halees, A.: Mining educational data to ımprove students’ performance: a case study. Int. J. Inf. Commun. Technol. Res. 2, 140–146 (2012)
Baradwaj, B.K., Pal, S.: Mining educational data to analyze students performance. Int. J. Adv. Comput. Sci. Appl. 2(6), 63–69 (2011)
Shanmuga Priya, K., Senthil Kumar, A.V.: Improving the student’s performance using educational data mining. Int. J. Adv. Netw. Appl. 04(04), 1680–1685 (2013)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Apoorva, S., Aditya S, K., Snigdha, P., Darshini, P., Sanjay, H.A. (2020). Prediction of Diabetes Mellitus Type-2 Using Machine Learning. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-37218-7_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37217-0
Online ISBN: 978-3-030-37218-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)