Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance evaluation of machinelearning techniques indiabetes prediction Subject The topic of the resource Artificial neural network; Logistic regression; Neural network with 10-fold; Random forest; Support vector machine Description An account of the resource Diabetes diagnosis is very important at preliminary stage rather than treatment. In todays world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features. BEIESP. Creator An entity primarily responsible for making the resource Raghavendra S.; Santosh Kumar J.; Raghavendra B.K. Source A related resource from which the described resource is derived International Journal of Engineering and Advanced Technology, Vol-8, No. 3, pp. 363-369. Publisher An entity responsible for making the resource available Blue Eyes Intelligence Engineering and Sciences Publication Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062716931&amp;partnerID=40&amp;md5=8894b68faf7c616fa11fe23d6a26271a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062716931&amp;partnerID=40&amp;md5=8894b68faf7c616fa11fe23d6a26271a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22498958 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raghavendra S., Department of Computer Science and Engineering at Christ Deemed To Be University, Bangalore, India; Santosh Kumar J., Department of Computer Science and Engineering at K.S.School of Engineering and Management, Bangalore, India; Raghavendra B.K., VTU Belgaum, Bangalore, Karnataka, India