Diabetes Prediction Using Machine Learning Techniques

Abhishek Khari; Anant Vajpeyi; Ankit Agrawal; Sargam Tiwari

Abstract

Diabetes mellitus or just diabetes is an ailment caused because of the expansion level of blood glucose. Diabetes is an interminable malady with the possibility to cause an overall human services emergency. In any case, early forecast of diabetes is very testing task for clinical specialists because of complex relationship on different factors as diabetes influences human organs, for example, kidney, eye, heart, nerves, foot and so on. AI is a developing logical field in information science managing the manners by which machines gain as a matter of fact. One such assignment is to help make expectations on clinical information. The point of this paper is to think about precision of various calculations and build up a framework which can perform early expectation of diabetes for a patient with a higher exactness.

Keywords

AI, Data Pre-processing, Classification, Machine Learning.

Reference

[1] M. W. Andy Liaw, "Classification and regression by randomForest," R news 2.3, pp. 18-22, 2002.

[2] P. J. Lin, "Clinical Multiple Chronic Conditions in Type 2 Diabetes Mellitus: Prevalence and Consequences," vol. 21, no. 1, 2015.

[3] Oliver Bear Don't Walk IV, David Joosten, Tim Moon , “Predicting the Diagnosis of Type 2 Diabetes Using Electronic Medical Records” , 2014

[4] Duyun Chen, Yaxuan Yang , and Junrui Zhang .” Methods for Predicting Type 2 Diabetes”, 2015

[5] Scikit-learn: http://scikit-learn.org/stable/

[6] A Library for Large LinearClassification: http://www.csie.ntu.edu.tw/~cjlin/lib linear/

[7] A Library for Support Vector Machines https://www.csie.ntu.edu.tw/~cjlin/libsvm/

[8] L. Breiman. Random forests. Machine learning, 45(1):5– 32, 2001.

[9] Flask: http://flask.pocoo.org

[10] Tensorflow Estimator, https://www.tensorflow.org/guide/estimators

Cites this article as

A. K. , A. V. , A. A. , S. T. , "Diabetes Prediction Using Machine Learning Techniques", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-5, Issue-6, Page No-240-241, 2018. Available from: