HariniSelvam-cse

HariniSelvam-cse / AI-Healthcare-Prediction-Dashboard

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AI-powered healthcare dashboard using Streamlit that integrates drug recommendation and symptom-based disease prediction with multi-model ML comparison and interactive visualizations.

16
0
69% credibility
Found Feb 17, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An interactive dashboard that recommends drugs based on patient vitals like age and blood pressure, and predicts diseases from selected symptoms, while showing comparisons of different prediction methods with charts.

How It Works

1
🔍 Discover the Tool

You find this friendly health prediction dashboard online, perfect for learning about drug suggestions and disease guesses from symptoms.

2
📥 Bring It Home

Download the files to a folder on your computer, ready to explore.

3
📁 Add Health Examples

Grab free public health data sets from trusted sites and drop them into your folder so the tool has examples to learn from.

4
🚀 Launch the Dashboard

Start the app and watch it open in your web browser, looking like a simple health checkup screen.

5
Choose Your Focus
💊
Suggest a Drug

Share details like age, gender, blood pressure, and more to get medicine ideas.

🦠
Check Symptoms

Tick off symptoms you're feeling to learn possible illnesses and risks.

6
✨ Make a Prediction

Enter the info, hit predict, and instantly see the suggested drug or disease name with a confidence percentage and helpful charts.

🎉 Health Insights Ready

Celebrate having clear visuals of how reliable the guesses are, all for educational fun and awareness.

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AI-Generated Review

What is AI-Healthcare-Prediction-Dashboard?

This Python Streamlit dashboard delivers AI-powered healthcare diagnostics, letting users input patient vitals like age, blood pressure, and cholesterol for drug recommendations, or check symptoms for disease predictions. It compares multiple ML models in real-time, spitting out confidence scores, risk levels, and interactive charts like accuracy bars and confusion matrices. Built for quick healthcare ML demos using scikit-learn and visualization libs, it solves the hassle of wiring up predictions from scratch.

Why is it gaining traction?

It bundles multi-model comparisons and viz right into a shareable Streamlit app, so you see which algorithm crushes it without extra setup—perfect for ai powered healthcare solutions prototypes. The sidebar inputs and instant predictions hook devs wanting polished, interactive demos over bare Jupyter notebooks. Low barrier to fork and tweak for custom ai powered healthcare tools.

Who should use this?

ML students prototyping symptom-based classifiers or drug recommenders for class projects. Healthcare devs building ai powered healthcare assistant project demos on Streamlit. Presenters needing a live dashboard to showcase model performance in talks or portfolios.

Verdict

Solid educational starter with 12 stars and a 0.7% credibility score—docs are clear, setup is standard Streamlit, but it's raw: no datasets included, hardcoded paths need fixing, zero tests. Use it as a forkable base for ai powered healthcare diagnostics if you're okay polishing for production.

(187 words)

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