Diabetes Prediction Analysis using Feature Selection

Authors: Gurwinder Kaur Bajwa, Anil Sagar, Baljinder Singh

Country: INDIA

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Abstract: Diabetes has influenced more than 246 million individuals worldwide with a dominant part of them being ladies. Recognition of diabetes in its beginning periods is the key for treatment. In this exploration work, number of choice trees is joined for the investigation procedure. This proposed research work also analyse the current computational insight strategies for anticipating diabetes. The dataset utilized in this examination work is gathered from National Institute of Diabetes and Digestive and Kidney Diseases and depends on Pima Indian Diabetic Set from University of California, Irvine (UCI) Repository of machine learning databases. Proposed technique is compared with ANN, SVM, KNN, naïve bayes and logistic regression algorithm. Proposed technique gives more accuracy, precision, recall, and f-measure and less errors as compared to existing algorithms and hence performs better.

Keywords: -

Paper Id: 296

Published On: 2018-09-13

Published In: Volume 6, Issue 5, September-October 2018

Cite This: Diabetes Prediction Analysis using Feature Selection - Gurwinder Kaur Bajwa, Anil Sagar, Baljinder Singh - IJIRMPS Volume 6, Issue 5, September-October 2018.

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