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SymptoSense is an ML-powered diagnostic platform that analyzes clinical inputs for Heart Disease, Diabetes, and Tumor prediction while generating explainable, medical-style reports with visual insights and patient comparisons.

Launch Live Deployment - https://symptosense-68xi.onrender.com/
Hosted on Render free tier — cold starts after inactivity may take 30–60 seconds.

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Features

Heart Disease Analysis Analyze cardiovascular health indicators with ML-powered risk prediction and visual comparisons.	Diabetes Prediction Evaluate glucose, BMI, insulin, and related metrics through explainable patient analysis.
Tumor Diagnostics Generate interpretable tumor analysis reports using clinical measurement distributions.	Explainable Reports Medical-style printable reports with feature importance graphs and patient comparisons.

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Sample Reports

Heart Disease Report

Diabetes Report

Tumor Analysis Report

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How To Use

Step	Description
1	Choose one of the available diagnostic systems: Heart Disease, Diabetes, or Tumor Analysis.
2	Fill in the required patient health parameters using the provided medical input forms.
3	Submit the form to allow the ML model to process the patient profile and generate predictions.
4	Analyze generated graphs, feature importance visualizations, and patient comparison metrics.
5	Export the generated medical-style report as a printable PDF document.

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Tech Stack

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ML Models Used

Diagnostic Module	Model	Purpose
Heart Disease Analysis	Random Forest Classifier	Predicts cardiovascular disease risk using patient clinical indicators.
Diabetes Prediction	Random Forest Classifier	Analyzes diabetic health patterns and glucose-related measurements.
Tumor Diagnostics	Logistic Regression	Classifies tumors as benign or malignant using clinical measurements.

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Local Setup

Setup	Description
Local Environment (venv)	1. Clone Repository git clone https://github.com/TheOnlySG/SymptoSense.git cd SymptoSense 2. Create Virtual Environment python -m venv venv Linux / macOS source venv/bin/activate Windows venv\Scripts\activate 3. Install Requirements pip install -r requirements.txt 4. Run Using Gunicorn gunicorn app.app:app Application will start on: http://localhost:8000
Docker Deployment	1. Build Docker Image docker build -t symptosense . 2. Run Docker Container docker run -p 5000:8000 symptosense Application will start on: http://localhost:5000

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License

This project is licensed under the MIT License.