Implementation of Machine Learning Algorithms for Analyzing Diabetes Disease

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. Our report focuses on quicker and efficient techniques for the diagnosis of diabetes disease. Traditional techniques like Random Forest Classifier had a few limitations in predicting the outcome of the disease. So we try to implement more advanced machine learning algorithms in our research like Naive Bayes, k-nearest neighbor and Decision Trees which give better predictions and increased accuracy. Along with these algorithms, we will be implementing many other algorithms too and be doing a comprehensive and a comparative study on these algorithms to get the best of all. This study can further help in determining which algorithms to use.