Volume 11 - Volume 11
Early Prognosis of Diabetes Using Supervised Learning Techniques: A Comparison of Performance
Abstract
Supervised machine learning algorithms have been a predominant technique in information mining
field. Disease forecast utilizing health information has shown a potential application region for these
techniques. This investigation centers to distinguish the vital patterns among various kinds of
supervised learning and their performance and utilization for diabetes prognosis. The point of this
work is to analyze the performance of various machine learning (ML) classifiers. These ML
classifiers are k-Nearest Neighbors, Support Vector Machine, ANN, Logistic Regression, Decision
Tree and Ensemble classifiers which are applied on diabetes datasets for evaluating performance.
The ML models are trained and tested with Pima Indian Diabetes dataset. The exploratory outcomes
uncover that the SVM classifier beat the different classifiers with a most noteworthy accuracy of 83%
in detecting diabetes.
Paper Details
PaperID: 2098
Author's Name: H.S. Niranjana Murthy
Volume: Volume 11
Issues: Volume 11
Keywords: Machine Learning, SVM, KNN, ANN, Ensemble Classifier.
Year: 2021
Month: May
Pages: 140-148