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– ABSTRACT
The remarkable advancements in biotechnology and public healthcare
infrastructures have led to a momentous production of critical and sensitive
healthcare data. By applying intelligent data analysis techniques, many interesting
patterns are identified for the early and onset detection and prevention of several
fatal diseases. Diabetes mellitus is an extremely life-threatening disease because
it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this
paper, a machine learning based approach has been proposed for the
classification, early-stage identification, and prediction of diabetes. Furthermore, it
also presents prediction based on different classification algorithm.For diabetes
classification, six different classifiers have been employed, i.e., random forest
(RF),Naïve bayes classifier (NB), logistic regression (LR), SVM classifier, Decision
tree, and KNN. For experimental evaluation, a benchmark PIMA Indian Diabetes
dataset is used.Here 80% data is used for training and remaining 20% data is
used testing. During the analysis, approach in many public healthcare
applications.
