abstract
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 naïve bayes algorithms. the research hopes to propose a quicker and more efficient
technique of diagnosing the disease, leading to timely treatment of the patients.