Diabetes mellitus is related to the high sugar level in the blood. According to the International Diabetes Federation
(IDF), there are currently 422 million diabetic people worldwide or 7.7% of the world's population, and this number is expected
to rise to 350 billion by 2030. Furthermore, 3.8 million deaths are attributable to diabetes complications every year with, an
annual increase of 2.7% from 1990. In this paper, we have proposed the system to predict diabetes using a machine learning
algorithm. Early detection of diabetes mellitus would lead to a decrease in the mortality rate. This paper presents an algorithm
for naïve Bayes and KNN, which we have implemented using C#. KNN gave the highest accuracy (100%) compared to other
algorithms. The other algorithms used are naïve Bayes, Decision tree, Logistic Regression, Random Forest, Support vector
machine. A dataset that we have used to build this product contains 21 columns. This product helps in decreasing the mortality
rate.
