DIABETES DISEASE PREDICTION USING MACHINE LEARNING

Abstract

This project explores the prediction of Diabetes
Disease utilizing an analysis of five supervised
machine learning algorithms: K-Nearest Neighbors
(KNN), Naïve Bayes, Decision Tree Classifier,
Random Forest, and Support Vector Machine (SVM).
Diabetes mellitus is the most common diseases
worldwide and keeps increasing every day due to
changing life style, unhealthy food habits like junk
foods and over weight problems. There were studies
handled in predicting diabetes mellitus through
physical and chemical tests are available for
diagnosing diabetes. Data science methods have the
potential benefits other scientific fields by shedding
new light on common questions. In the proposed
system an efficient way of detecting diabetes is
proposed through Machine Learning and Deep
Learning. Under machine learning we used the
classification algorithm SVM & NN for DL
algorithm. Experiment results shows that the
prediction of diabetes done at high accuracy.
Diabetes is a disease caused due to increase level
of blood glucose.