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Diabetes Detection using Machine Learning

V.Pavan Ganesh

2111cs020331@mallareddyuniversity.ac.in

A.Pavan Kumar

2111cs020333@mallareddyuniversity.ac.in

S.Pavan Kumar

2111cs020335@mallareddyuniversity.ac.in

K.Pavan Kumar Reddy

2111cs020332@mallareddyuniversity.ac.in

Ch.Pavan kumar

2111cs020334@mallareddyuniversity.ac.in

V.Pavan Kumar

2111cs020336@mallareddyuniversity.ac.in

Prof  T.Ramya

Department of CSE (AI&ML)

MALLA REDDY UNIVERSITY

HYDERABAD

Abstract: Our project aims to create a user-friendly Diabetes Detection System using Machine Learning. The system predicts the likelihood of diabetes based on essential health parameters. It leverages popular algorithms like Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Gradient Boosting. The user inputs information like glucose levels, BMI, and age, and the system provides a prediction along with the percentage confidence. The graphical user interface simplifies interaction, making it accessible to users. The project's potential lies in its ability to aid in early diabetes detection, allowing for timely preventive measures. Future enhancements could include real-time monitoring and collaboration with healthcare providers for a more comprehensive health assessment.Our project focuses on developing an intuitive Diabetes Detection System using Machine Learning. It employs robust algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Gradient Boosting.

Keywords: diabetes detection, BMI(body mass index), pregnancies, insulin, age, skin thickness, glucose, blood pressure, diabetes pedigreefunction