1. Abstract

Federated Learning for Multi-Hospital Disease Prediction is a machine learning project that enables multiple hospitals to collaboratively train disease prediction models while keeping patient data within their respective institutions. Traditional healthcare systems require centralized data collection, which raises privacy, security, and data-sharing concerns. The proposed system uses Federated Learning to train local models at different hospitals and combines their model updates using Federated Averaging (FedAvg) to create a global prediction model. The system can support disease prediction for conditions such as diabetes, heart disease, and chronic kidney disease using patient health records. By reducing the need to transfer sensitive medical data, the project aims to improve collaborative healthcare analytics, protect patient confidentiality, and support more reliable disease prediction.

PMC

+1

2. Objectives

1. To develop a federated learning framework for disease prediction across multiple hospitals.
2. To protect patient privacy by keeping raw medical records at their original locations.
3. To train local machine learning models using individual hospital datasets.
4. To combine local model updates using the Federated Averaging (FedAvg) algorithm.
5. To improve prediction performance by learning from distributed healthcare data.
6. To evaluate model performance using accuracy, precision, recall, and F1-score.
7. To provide a user-friendly interface for entering patient information and displaying prediction results.

3. Existing System

In the existing system, hospitals generally develop disease prediction models using their own medical datasets or rely on centralized machine learning systems that collect data from multiple sources. Individual hospital models may have limited training data, reducing their ability to generalize across different patient populations. Centralized approaches require medical records to be transferred or consolidated, creating privacy, security, regulatory, and data management challenges.

Disadvantages

1. Limited collaboration between hospitals.
2. Increased privacy risks when transferring sensitive patient information.
3. Difficulty accessing diverse medical datasets.
4. Potentially lower prediction performance when training data is limited.
5. Data integration and standardization challenges.
6. Increased security and compliance requirements.

4. Proposed System

The proposed system uses Federated Learning to enable multiple hospitals to collaboratively build a global disease prediction model without directly exchanging raw patient records. Each hospital trains a local model using its own dataset and sends model updates to a central aggregation server. The server combines these updates using the FedAvg algorithm and distributes the updated global model back to participating hospitals.

The system can incorporate additional privacy mechanisms, such as secure aggregation and differential privacy, to strengthen protection of model updates. A web-based interface can allow users to enter patient health information and view model-generated predictions.

Key Features

1. Multi-hospital federated model training.
2. Privacy-aware data processing.
3. FedAvg-based model aggregation.
4. Disease prediction using machine learning.
5. Model performance evaluation and visualization.
6. Web-based prediction interface.
7. Support for future integration of additional hospitals.

5. Implementation Procedure

1. Dataset Collection: Obtain suitable, de-identified healthcare datasets containing patient attributes and disease labels.

2. Data Preprocessing: Handle missing values, remove duplicates, encode categorical variables, and normalize numerical features.

3. Hospital Simulation: Divide the dataset into separate partitions representing multiple hospitals, ensuring that each hospital retains its own local data.

4. Local Model Development: Train a machine learning model, such as Logistic Regression, Random Forest, or a neural network, independently at each hospital.

5. Federated Learning Setup: Configure the federated learning framework to coordinate communication between hospital clients and the aggregation server.

6. Model Aggregation: Use Federated Averaging (FedAvg) to combine local model updates into a shared global model.

7. Iterative Training: Distribute the global model to the hospitals and repeat local training and aggregation for multiple rounds.

8. Disease Prediction: Use the trained model to generate predictions from supported patient health inputs.

9. Performance Evaluation: Evaluate the model using accuracy, precision, recall, F1-score, and confusion matrices.

10. Web Application Development: Develop a user interface using Flask or Streamlit to display predictions, model metrics, and training progress.

11. Privacy Evaluation: Verify that raw patient records are not transmitted to the aggregation server and, if implemented, evaluate the configured privacy mechanisms.

6. Software Requirements

7. Hardware Requirements

Recommended for smoother training: Intel Core i5 or equivalent, 16 GB RAM, and SSD storage. A GPU is optional for traditional machine learning models.

8. Advantages of the Project

1. Improved Patient Privacy: Raw patient records remain with their respective hospitals.
2. Collaborative Learning: Hospitals can benefit from knowledge learned across multiple datasets.
3. Better Generalization Potential: Diverse hospital data can help the global model learn broader patient patterns.
4. Reduced Data Transfer: Only model updates need to be communicated during training.
5. Scalability: Additional hospital clients can be incorporated into the framework.
6. Flexible Model Development: Different machine learning algorithms can be evaluated.
7. Performance Monitoring: Metrics can be tracked across federated training rounds.
8. Future Expansion: The system can be extended with secure aggregation, differential privacy, and additional disease categories.