Mountain Top University Library

The aim of this study was to develop a model required for classifying diabetes in a patient. The study identified the dataset containing various features and attributes, formulated the model and simulated the model, evaluated the model’s performance and
implemented the model using supervised machine learning algorithm. A review of the literature was being done to identify and understand existing classification and prediction models. The user and model requirements of the model were identified from diabetic and non-diabetic patients using questionnaires. The model formulation and simulation were done using decision tree algorithm in Python Google CoLab. The model was implemented using Anaconda. The implementation of the frontend was done using Spyder and Streamlit. The backend was implemented using Python. The result of the model after implementation
showed the web application for the prediction of diabetes. The results revealed that the system was able to predict the presence of diabetes. The study concluded that using the system model in health care sectors and facilities, it will help in the early detection and
treatment of diabetes among patients. Keywords: Diabetes Mellitus, Prediction model, Classification model, Supervised
Machine Learning Algorithms, Predictive model