The prediction analysis is the approach which can predict the future possibilities based on the current information. The diabetes prediction is the approach which is applied to predict the diabetes based on the various attributes. The diabetes dataset has various attributes and based on that attributes diabetes can be predicted. In the previous year’s approach of SVM is applied for the diabetes prediction. To improve accuracy of diabetes prediction voting based classification is applied in this paper. The proposed model is implemented in python and results are analyzed in terms of accuracy, execution time.

Diabetes, SVM, Voting

[1] Abdelghani Bellaachia and Erhan Guven (2010), “Predicting Breast Cancer Survivability Using Data Mining Techniques”, Washington DC 20052, vol. 6, 2010, pp. 234-239.
[2] Azhar Rauf, Mahfooz, Shah Khusro and Huma Javed (2012), “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity”, Middle-East Journal of Scientific Research, vol. 12, 2012, pp. 959-963.
[3] Min Chen, Yixue Hao, Kai Hwang, Fellow, IEEE, Lu Wang, and Lin Wang (2017), “Disease Prediction by Machine Learning over Big Data from Healthcare Communities”, 2017, IEEE, vol. 15, 2017, pp- 215-227
[4] Akhilesh Kumar Yadav, Divya Tomar and Sonali Agarwal (2014), “Clustering of Lung Cancer Data Using Foggy K-Means”, International Conference on Recent Trends in Information Technology (ICRTIT), vol. 21, 2013, pp.121-126.
[5] Kajal C. Agrawal and Meghana Nagori (2013), “Clusters of Ayurvedic Medicines Using Improved K-means Algorithm”, International Conf. on Advances in Computer Science and Electronics Engineering, vol. 23, 2013, pp. 546-552.
[6] [10] Chew Li Sa, Bt Abang Ibrahim, D.H., Dahliana Hossain, E. and bin Hossin, M. (2014), "Student performance analysis system (SPAS)", in Information and Communication Technology for The Muslim World (ICT4M), 2014 The 5th International Conference on, vol.15, 2014, pp.1-6.
[7] Han Wu, Shengqi Yang, Zhangqin Huang, Jian He, Xiaoyi Wang, “Type 2 diabetes mellitus prediction model based on data mining”, ScienceDirect, Vol. 11, issue 3, pp. 12-23, 2018.
[8] Prova Biswas, Ashoke Sutradhar, Pallab Datta, “Estimation of parameters for plasma glucose regulation in type-2 diabetics in presence of meal”, IET Syst. Biol., 2018, Vol. 12 Iss. 1, pp. 18-25, 2018.
[9] Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research”, Computational and Structural Biotechnology Journal 15 (2017) 104–116
[10] Zhiqiang Ge, Zhihuan Song, Steven X. Ding, Biao Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning”, 2017 IEEE. Translations and content mining are permitted for academic research only, vol. 5, pp. 20590-20616, 2017.
[11] Alexis Marcano-Cede˜no, Diego Andina, “Data mining for the diagnosis of type 2 diabetes”, IEEE, Vol. 11, issue 3, pp. 9-19, 2016.
[12] Bayu Adhi Tama, Afriyan Firdaus, Rodiyatul FS, “Detection of Type 2 Diabetes Mellitus with Data Mining Approach Using Support Vector Machine”, Vol. 11, issue 3, pp. 12-23, 2008.
[13] Aiswarya Iyer, S. Jeyalatha and Ronak Sumbaly, “DIAGNOSIS OF DIABETES USING CLASSIFICATION MINING TECHNIQUES”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.1, 2015.