MediPredict AI – Breast Cancer Detection Web App with Machine Learning
A modern Flask-based web application that leverages machine learning to predict breast cancer (Benign/Malignant) using 30 cellular features. Features responsive UI, probability scores, input validation, and glass-effect design.
Technology Used
Flask | Python | Scikit-learn | HTML5 | CSS3 | JavaScript | Glassmorphism UI | Pickle (for model serialization)
Project Files
MediPredict AI is a cutting-edge breast cancer detection web application developed using Python’s Flask framework and powered by machine learning models. This tool is designed to assist healthcare professionals in early diagnosis by analyzing cellular features and providing accurate predictions.
Project Overview
MediPredict AI uses the power of Scikit-learn to train a model on the Wisconsin Diagnostic Breast Cancer dataset. It offers real-time predictions based on user-provided cellular measurements, classifying tumors as either Benign or Malignant along with probability scores.
Key Features
- Real-Time Prediction: Instantly detect tumor type as Benign or Malignant.
- Probability Scores: Understand confidence levels behind each prediction.
- 30 Cellular Features: Support for Mean, Standard Error, and Worst values across 10 critical parameters.
- Modern UI/UX: Built with responsive HTML5, CSS3, and JavaScript featuring glassmorphism design.
- Error Handling: Robust validation ensures clean data inputs and error-free processing.
- Cross-Platform: Fully mobile-friendly interface for use on any device.
Applications & Use Cases
- Medical diagnostics and early disease detection
- Educational tool for students studying ML in healthcare
- Prototype for building advanced medical AI applications
- Integration into hospital diagnostic systems (with enhancements)
Technology Stack
- Backend: Flask (Python)
- Frontend: HTML5, CSS3, JavaScript
- Machine Learning: Scikit-learn
- UI Design: Custom CSS with Glass Effect
How It Works
- User fills in the form with cellular feature values
- Data is sent to the Flask backend
- Model processes the input and returns a prediction
- Result is displayed with detailed probability scores
Installation Guide
Follow our step-by-step guide to deploy MediPredict AI locally. Requires Python 3.7+, pip, and virtual environment support.
Why Choose MediPredict AI?
- Highly customizable for further development
- Excellent foundation for health-tech projects
- Well-documented codebase with clean structure
- Easy to extend with new features like authentication or API integration
Who Should Buy This Project?
- Students working on final year projects
- Developers looking to build AI-powered tools
- Startups aiming to prototype medical tech solutions
- Hobbyists exploring machine learning in healthcare
Get the full source code starting at ₹99 and unlock additional services like project setup, report generation, and detailed explanations. Ideal for anyone interested in integrating AI into real-world healthcare applications.
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Project Setup
We'll install and configure the project on your PC via remote session (Google Meet, Zoom, or AnyDesk).
Source Code Explanation
1-hour live session to explain logic, flow, database design, and key features.
Want to know exactly how the setup works? Review our detailed step-by-step process before scheduling your session.
₹999
Custom Documents (College-Tailored)
- Custom Project Report: ₹1,200
- Custom Research Paper: ₹800
- Custom PPT: ₹500
Fully customized to match your college format, guidelines, and submission standards.
Project Modification
Need feature changes, UI updates, or new features added?
Charges vary based on complexity.
We'll review your request and provide a clear quote before starting work.