Smart Diagnosis: A Machine Learning Approach for Early Detection of Diabetes Using Clinical Parameters

The Diabetes Detection System is a machine learning-powered diagnostic tool designed to facilitate early identification of diabetes risk using structured patient data. The platform leverages statistical models to analyze clinical attributes such as glucose levels, blood pressure, BMI, and age to predict the likelihood of diabetes. Built using a lightweight and modular architecture, the system employs a Flask-based backend, a re- sponsive frontend developed with HTML5, CSS3, and Bootstrap, and integrates a Logistic Regression model trained on the PIMA Indian Diabetes dataset. Key components of the system include comprehensive data preprocessing techniques—such as feature selection using corre- lation matrices, outlier treatment using interquartile ranges, and feature scaling via StandardScaler—to enhance model accuracy and reliability. The frontend interface, implemented using Flask and optionally Tkinter for desktop integration, allows users to input medical parameters and receive real-time predictions. The application underwent functional testing to ensure accu- racy, robustness, and responsiveness across devices. Hyperparam- eter tuning using GridSearchCV further optimized the prediction performance, achieving a classification accuracy of over 80 This project presents a scalable, user-friendly, and medically relevant solution for early diabetes screening. Its modular design provides a foundation for future enhancements such as multi- disease detection, integration with real-time health monitoring devices, and deployment on cloud-based platforms for broader accessibility.