Diadisc: A Machine Learning Approach for Classification and Prediction of Diabetes Mellitus

Diabetes is a category of metabolic illnesses characterized by persistently elevated blood sugar levels. Frequent urination, increased thirst, and increased hunger are all signs of elevated blood sugar. Diabetes can lead to a wide range of consequences if ignored. To accurately predict whether or not the patients in the data set have diabetes, we are trying to create a machine-learning model. Our goal in this study is to determine a patient’s likelihood of having diabetes based on a variety of factors, including glucose level, insulin, age, and BMI. Our goal in this study is to determine a patient’s likelihood of having diabetes based on a variety of factors, including glucose level, insulin, age, and BMI. We will complete every stage, including data collection and model implementation. We compare multiple machine learning algorithms using the accuracy score measure during the model evaluation process to see which is better. Then, we use Flask, a Python micro framework, to develop a web application.