HAKSAL26/AI-FOR-MEDICAL-IMAGING-AND-PRECISION-DIAGNOSTICS-FROM-THEORY-TO-PRACTICE

This study examines the role of artificial intelligence in improving precise and accurate diagnosis is low-resource settings where delayed or inaccurate diagnosis continue to increase morbidity and mortality rate # AI-Powered Diagnostics Platform React frontend presenting the abstract, methodology, and expected outcomes for an AI-powered diagnostics initiative in low-resource healthcare settings. Python backend provides dataset-based model training and prediction APIs. ## Scripts - `npm install` - `npm start` - `npm run build` ## Backend (Node.js) ### Setup - Run `npm install` to install all dependencies (frontend + backend). - Start the API server with `npm run server`. ### API Endpoints (Node.js) - `GET /health` – health check - `POST /train?target= ` – upload CSV and train a model - `POST /predict` – send JSON records to get predictions ### Dataset Requirements - CSV format. - `target` query parameter must match a column in the CSV. - Other columns are treated as features (numeric and categorical supported). ### Example Predict Body ```json { "records": [ {"age": 29, "symptom_score": 3.2, "region": "rural"}, {"age": 41, "symptom_score": 1.7, "region": "urban"} ] } ``` ### Example Chat Body ```json { "message": "I have a sore throat and fever for two days" } ``` ## Pages - Overview: Abstract and impact highlights - Methodology: Phased implementation approach - Outcomes: Expected health and system benefits - Assistant: Prototype chat experience for user interaction with an eventual trained model ## Local Model Training (Python) This project includes a local text model for symptom → condition classification. ### 1) Download datasets Download the Kaggle datasets and place the CSV files in a local folder such as `data/raw/`: - kaggle.com - kaggle.com - kaggle.com - kaggle.com ### 2) Train the model Run the training script (uses scikit-learn locally): - `python ml/train_symptom_model.py --da …