Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.

Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.

A comprehensive machine learning application that predicts breast cancer malignancy using cytology measurements. Features an interactive Streamlit web interface with real-time visualizations including radar charts for cell nuclei analysis. Implements logistic regression with data preprocessing pipelines for accurate benign/malignant classification.

This project analyzes breast cancer data to predict tumor malignancy using machine learning models, including regression and classification techniques. It features data visualization, preprocessing, and an interactive Streamlit app for exploration and prediction.

Multiple Disease Prediction System is a supervised Machine Learning Model in Python. I have also deployed this machine learning web app using Streamlit. This web app can predict the diseases of a human such as Diabetes, heart disease, Parkinson's and Breast Cancer using Machine Learning model.

This proposed projec presents a comparison of six machine learning algorithms: XGBoost Classifier, Random Forest, KNN Classifier, Logistic Regression, SVM Classification, Decision Tree. Our research led to 94.96% accuracy.

Breast Cancer Detection App using Streamlit A machine learning web application that predicts breast cancer based on input features. Built with Python, Streamlit, and scikit-learn. Users can input patient data and get predictions instantly.

A Machine Learning project to classify breast cancer tumors as Malignant or Benign using Logistic Regression. Includes a Streamlit web app for interactive predictions.

Flask based Web app with 5 Machine Learning Models including 10 most common Disease prediction and Coronavirus prediction with their symptoms as inputs and Breast cancer , Chronic Kidney Disease and Heart Disease predictions with their Medical report as inputs

A project of breast cancer prediction using machine learning we have made in it.we have used logistic regression model.A flask app for backend ,& you can run this on VS code.

Modular Streamlit app for breast cancer diagnosis. Train or compare models, analyze data, make predictions, and visualize feature importance. Supports scikit-learn, TensorFlow, and more. User-friendly, flexible, and designed for interactive machine learning workflows.

A multi-model machine learning web application built using Streamlit that integrates multiple predictive systems including Calories Burnt Prediction, Diabetes Detection, and Breast Cancer Classification. The app provides an intuitive UI with a modular architecture, enabling seamless switching between models and real-time predictions.

CancerGuardian is a machine learning-powered web app that helps predict breast cancer diagnoses based on cytology measurements. 🩺✨ Built with Streamlit, Scikit-Learn, and Plotly, this tool visualizes tumor characteristics and provides predictions using a trained model. 🚀

This project aims to assist in the early detection of breast cancer using Machine Learning (ML) techniques. Early diagnosis is crucial for improving survival rates, and this project provides a simple yet effective web-based prediction app that can classify whether a breast tumor is malignant or benign based on input medical data.

A complete machine learning project that trains a Support Vector Machine (SVM) model on breast cancer cell data and deploys it in a user-friendly Streamlit web app for real-time predictions. This project demonstrates the full workflow from data preprocessing and visualization to model training, evaluation, saving, and interactive deployment.

A lightweight machine learning project that predicts treatment response (good vs poor) using clinical-style tumor features from the built-in breast cancer dataset. It includes a Streamlit app where you can select a patient, view prediction probabilities, and see the top features influencing the decision.

This project uses Django and Python's Machine Learning libraries for breast cancer detection, involving data cleaning, feature selection, and evaluating Scikit-learn models (Logistic Regression, Decision Trees, Random Forests, SVM). The best model, saved with Joblib, is integrated into a Django app for real-time patient data predictions.

this project predict breast cancer type based on the attached features in the datasets

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Breast Cancer Prediction App using Machine Learning and Streamlit

Final Year Project

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Machine Learning project app for breast cancer prediction using classification algorithms

Flask app for breast cancer prediction using machine learning models and Deep Learning Models

Breast Cancer Prediction ML web app: Breast Cancer Prediction Web App Overview Welcome to our Breast Cancer Prediction Web App, a cutting-edge machine learning application designed to assess the risk of breast cancer based on crucial health indicators. This user-friendly tool .