This website provides a platform for users to predict their likelihood of developing diabetes based on various factors.

This repository implements a K-Nearest Neighbors (KNN) classifier using Scikit-Learn to predict the onset of diabetes. The model utilizes the Pima Indians Diabetes Database to predict the likelihood of diabetes. The code includes data preprocessing, model training, evaluation, and visualization of results.

Frontend for DIAWeb, an artificial intelligence powered web app for finding diabetes risk

Nested cross-validation implementation for the binary classification of healthy vs. diabetic patients.

A machine learning project to predict diabetes using a Support Vector Classifier model. It includes data preprocessing, model training, evaluation, and a Flask web application for real-time predictions.

This project predicts diabetes using the PIMA Diabetes dataset, leveraging health metrics of Pima Indian women. Machine learning models like Logistic Regression, Decision Trees, and Random Forest are implemented to determine the likelihood of diabetes, evaluated using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.

A comprehensive project to predict and analyze diabetes health data using advanced machine learning models, including Logistic Regression, Random Forest, and XGBoost. 📊🔍

Diabetes Prediction Using SVM Algorithm

This repository contains code for building a K-Nearest Neighbors (KNN) model to predict diabetes based on patient data. Includes data cleaning, hyperparameter tuning, and evaluation metrics.

[ACIIDS 2024] A Deep Learning Approach to Diabetes Diagnosis

This project uses machine learning to predict diabetes and provides explanations through SHAP and PCA, displayed in an intuitive user interface.

Implementing, describing and testing a single layer perceptron for predicting diabetes

CNN system analyzes retinal images & provides instant diagnoses, improving accuracy & reliability. The platform features a user-friendly interface implemented with Flask, allowing easy accessibility for users to upload images & to receive results.

A machine learning web application built using Streamlit that predicts whether or not a patient has diabetes.

This web app uses a machine learning model to predict the risk of diabetes based on patient data.

A Python-based system to predict diabetes using Machine Learning with Support Vector Machine (SVM). Includes data preprocessing, model training, and evaluation to achieve high prediction accuracy.

A native iOS App (Swift) to help diabetics. Determine the number of units or sugar to take to be in a specific glycemic target. Determine the total parts of carbohydrates in a specific portion of food. And a lot of personalization settings.

Early_Stage_Diabetes_Detection using Machine Learning