vaibhu1 / frt-project

Diabetes-prediction

Project Demo URL : https://frt-azure-2.azurewebsites.net

•This repository consists of files required to deploy a Web App created with Flask on Microsoft Azure.# diabetes_predictor The project helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of a Kaggle database.

By using the statistical data about how certain aspects like BMI, Glucose level, Insulin level, age etc. impact if an individual is prone to diabetes or not, the project will be able to tell the user if the person has diabetes or not by entering those values. So in a way the project will help in monitoring the likelihood of someone developing diabetes. The project can be extended to include other diseases prediction which I will incorporate later down the road.

Problem Statement/Oppurtunity:

Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques.

Project Discription:

Machine learning techniques Provide better result for prediction by con- structing models from datasets collected from patients. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. Which are K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting (GB) and Random Forest (RF). The accuracy is different for every model when compared to other models. The Project work gives the accurate or higher accuracy model shows that the model is capa- ble of predicting diabetes effectively. Our Result shows that Random Forest achieved higher accuracy compared to other machine learning techniques. The is a Microsoft Azure Web App project that helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of Kaggle database.

Primary Ajure Technology:

Azure Web Apps , AI+Machine Learning, Computer Vision, Static Web Apps, Web Apps

Conclusion:

Hence we succesfully predict that any person having diabities or not.

Thank You