Abstract

AbstractDiabetes has emerged as one of the most deadly and prevalent illnesses in the modern society, not just in India but also everywhere else. Diabetes now impacts individuals of every age and is associated with lifestyle, genetics, stress, and ageing. Different types of machine learning approaches are now applied to forecast diabetes and also the disorders brought on by this disease. In this study we have used five machine learning classifiers such that Extra Tree (ET),Decision Tree (DT),Random Forest (RF), K-Nearest Neighbour (KNN) and Passive Aggressive Classifier (PAC) for diabetes mellitus prediction. The experimental findings demonstrate that Random Forest and Extra Tree have the lowest error rates with the highest accuracy (81.16%).KeywordsDiabetesMachine learningAccuracyClassifiers

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.