This paper is published in Volume-2, Issue-5, 2016
Area
Machine Learning
Author
Rabina, Er. Anshu Chopra
Org/Univ
S.S.C.E.T, Badhani,Punjab, India
Pub. Date
03 September, 2016
Paper ID
V2I5-1136
Publisher
Keywords
Data mining, weka, Prediction, machine learning.

Citationsacebook

IEEE
Rabina, Er. Anshu Chopra. Diabetes Prediction by Supervised and Unsupervised Learning with Feature Selection, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Rabina, Er. Anshu Chopra (2016). Diabetes Prediction by Supervised and Unsupervised Learning with Feature Selection. International Journal of Advance Research, Ideas and Innovations in Technology, 2(5) www.IJARIIT.com.

MLA
Rabina, Er. Anshu Chopra. "Diabetes Prediction by Supervised and Unsupervised Learning with Feature Selection." International Journal of Advance Research, Ideas and Innovations in Technology 2.5 (2016). www.IJARIIT.com.

Abstract

: Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
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