  Diabetes has affected over 246 million people worldwide with a majority of
them being women. According to the WHO report, by 2025 this number is expected
to rise to over 380 million. The disease has been named the fifth deadliest
disease in the United States with no imminent cure in sight. With the rise of
information technology and its continued advent into the medical and healthcare
sector, the cases of diabetes as well as their symptoms are well documented.
This paper aims at finding solutions to diagnose the disease by analyzing the
patterns found in the data through classification analysis by employing
Decision Tree and Na\"ive Bayes algorithms. The research hopes to propose a
quicker and more efficient technique of diagnosing the disease, leading to
timely treatment of the patients.
