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Multiple Disease Prediction System using Machine Learning

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Multiple Disease Prediction System using Machine Learning

Objective: Develop a machine learning system capable of predicting multiple diseases from patient data.

Context: In India, early detection of various diseases is essential for effective treatment, but access to comprehensive diagnostic tools is limited in many regions. An integrated prediction system can empower healthcare providers and patients alike.

What you'll build: You'll preprocess and normalize health datasets, implement and train multi-label classification models using scikit-learn and NumPy, and design a user interface with Flask for frontend integration. The system will be tested and refined for reliable multi-disease prediction.

Deliverables: End-to-end web application with prediction functionality and user interface.

Milestones

1. Dataset preprocessing

10 marks 6d

Clean and preprocess the provided disease datasets by handling missing values, encoding categorical variables, and normalizing features as appropriate. Submit a Jupyter notebook or Python script showing your preprocessing steps, along with a brief summary of the resulting dataset shape and feature types. Ensure the data is ready for input into machine learning models.

2. Model selection & training

10 marks 6d

Select at least two suitable machine learning algorithms for multi-disease classification, and train them on your preprocessed data. Submit your code, training process documentation, and a table comparing key performance metrics (accuracy, precision, recall) on a validation set. Clearly indicate which model you will use for deployment based on these results.

3. Prediction logic implementation

10 marks 6d

Implement the prediction logic that takes user input, processes it to match your model's expected format, and returns the predicted disease(s). Submit the code for this logic, including input validation and mapping model outputs to disease names. Demonstrate correct predictions on at least three sample input cases.

4. UI design

10 marks 6d

Design a simple, user-friendly interface (web or desktop) that allows users to enter relevant health parameters and view prediction results. Submit screenshots and code for the UI, ensuring all required input fields are present and results are clearly displayed. The UI must successfully connect to your prediction logic.

5. Integration and testing

10 marks 6d

Integrate your trained model, prediction logic, and UI into a single working application. Submit a video or set of screenshots showing end-to-end functionality, along with a test report detailing at least five test cases (valid and invalid inputs) and their outcomes. The system must handle errors gracefully and provide accurate predictions.

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Upcoming sessions

Session	Window	Enrolled
Multiple Disease Prediction System using Machine Learning	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Multi-label classificationData normalizationFrontend integration

Tools used

Python Flask scikit-learn NumPy

Prerequisites

Classification algorithms Health-related datasets

Available mentors

Priyang Kumar

Free

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AICTE-aligned Project Completion Certificate

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Multiple Disease Prediction System using Machine Learning

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