Abstract- The many advancements in the healthcare and technological infrastructure has developed in bioscience which led to an incredible production of healthcare of critical and sensitive data. By using various data techniques and many patterns for identify and early onset detection and prevent many fatal diseases.

Diabetes mellitus is a most occurring  life threatening disease because it also cause  to other l diseases, i.e., heart, kidney, liver and nerves system  damage. Here, few machine learning algorithms based approaches has been  proposed for the categorization, for early stage prediction and identification of diabetes.

For diabetes classification, we used six different classifiers they are i.e., Logistic Regression, KNN Classifier, Naïve. Bayes classifier, SVM classifier, Decision Trees, random forest classifier. We will be using a prediction of diabetes model for better classification of diabetes which consists few different factors required for diabetes along with basic factors based as Glucose, Body Mass Index, Age, Insulin, etc.

Training and testing will be done to get the possible accurate results on the data set considered.

 Index Terms- Logistic Regression , KNN Classifier, naïve bayes classifier, SVM classifier, decision tree classifier,  random forest classifier.

PDF | DOI: 10.17148/IJARCCE.2023.125141

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