[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-81188":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":8,"htmlUrl":8,"language":9,"languages":8,"totalLinesOfCode":8,"stars":10,"forks":11,"watchers":12,"openIssues":11,"contributorsCount":11,"subscribersCount":11,"size":11,"stars1d":11,"stars7d":13,"stars30d":13,"stars90d":11,"forks30d":11,"starsTrendScore":11,"compositeScore":14,"rankGlobal":8,"rankLanguage":8,"license":8,"archived":15,"fork":15,"defaultBranch":16,"hasWiki":17,"hasPages":15,"topics":18,"createdAt":8,"pushedAt":8,"updatedAt":19,"readmeContent":20,"aiSummary":21,"trendingCount":11,"starSnapshotCount":11,"syncStatus":22,"lastSyncTime":23,"discoverSource":24},81188,"CardioIntel","YogeshRajkumar\u002FCardioIntel","YogeshRajkumar",null,"JavaScript",28,0,27,1,34.6,false,"main",true,[],"2026-06-12 04:01:32","# 🫀 CardioIntel AI: Clinical-Grade Cardiovascular Diagnostics\n\n![CardioIntel Logo](FrontEnd\u002Fpublic\u002Ffavicon.svg)\n\n[![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.10%2B-blue?logo=python&logoColor=white)](https:\u002F\u002Fwww.python.org\u002F)\n[![React](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FReact-18.x-61DAFB?logo=react&logoColor=black)](https:\u002F\u002Freactjs.org\u002F)\n[![Flask](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFlask-3.0-000000?logo=flask&logoColor=white)](https:\u002F\u002Fflask.palletsprojects.com\u002F)\n[![Gemini](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGoogle-Gemini_AI-4285F4?logo=google&logoColor=white)](https:\u002F\u002Fdeepmind.google\u002Ftechnologies\u002Fgemini\u002F)\n[![MongoDB](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMongoDB-Atlas-47A248?logo=mongodb&logoColor=white)](https:\u002F\u002Fwww.mongodb.com\u002F)\n\n**CardioIntel AI** is a state-of-the-art, full-stack medical intelligence platform designed for high-accuracy heart disease risk assessment. By combining an ensemble of advanced machine learning models with the generative power of **Google Gemini**, CardioIntel transforms raw clinical data into actionable, patient-centric health insights.\n\n---\n\n## 🚀 Core Features\n\n### 🔬 Intelligent Diagnostics\n*   **Ensemble Engine**: Combines **Random Forest**, **XGBoost**, and **LightGBM** using soft-voting for superior predictive reliability.\n*   **Explainable AI (XAI)**: Native integration of **SHAP** and **LIME** to provide transparency into *why* the model assigned a specific risk level.\n*   **Clinical Gauge**: Real-time visualization of risk probability with calibrated confidence intervals.\n\n### 🧠 AI Clinical Explainer\n*   **Gemini-Powered Narratives**: Automatically translates complex biometric patterns into natural, medically-grounded clinical summaries.\n*   **Risk Multiplication Logic**: Explains the interaction between different biomarkers (e.g., how elevated BP multiplies the risk of high cholesterol).\n*   **Urgency Assessment**: Context-aware urgency levels (Immediate, Moderate, Routine) based on the severity of identified clinical flags.\n\n### 🤖 AI Health Partner\n*   **Contextual Chat**: A dedicated AI assistant that knows your specific clinical history and answers questions about diet, routine, and risk factors.\n*   **Actionable Advice**: Provides personalized lifestyle directives, including salt reduction strategies, exercise approval checks, and dietary improvements.\n\n### 📄 Clinical Reporting\n*   **Auto-Generated Reports**: Professional, print-ready PDF-style clinical reports featuring patient profiles, biometric trends, and diagnostic narratives.\n*   **Patient History Tracking**: Persistent session management using **MongoDB** to track health progress over time.\n\n---\n\n## 🏗️ System Architecture\n\n```mermaid\ngraph TD\n    subgraph \"Frontend (React + Vite)\"\n        UI[User Console] --> Predict[Diagnostic Form]\n        UI --> Assistant[AI Health Partner]\n        UI --> Report[Clinical Documentation]\n    end\n\n    subgraph \"Cloud Intelligence\"\n        Gemini[Google Gemini API] --> Narratives[Clinical Summaries]\n        Gemini --> Chat[Interactive Support]\n    end\n\n    subgraph \"Backend (Flask)\"\n        API[Inference API] --> ML[Ensemble Model]\n        API --> DB[(MongoDB Atlas)]\n        ML --> XAI[SHAP \u002F LIME]\n    end\n\n    Predict -->|Clinical Features| API\n    Assistant -->|Natural Language| Gemini\n    API -->|Risk Data| UI\n    Gemini -->|Interpretations| UI\n```\n\n---\n\n## 🛠️ Tech Stack\n\n### Artificial Intelligence & ML\n-   **Models**: Scikit-Learn (RF), XGBoost, LightGBM (Ensemble)\n-   **GenAI**: Google Gemini (via `google-generativeai`)\n-   **Interpretability**: SHAP, LIME\n-   **Preprocessing**: Pandas, NumPy, Scikit-Learn Scalers\n\n### Backend (Python)\n-   **Framework**: Flask 3.0\n-   **Database**: MongoDB (Atlas)\n-   **Serialization**: Joblib\n-   **Environment**: Python-dotenv\n\n### Frontend (JavaScript)\n-   **Framework**: React 18 (Vite)\n-   **Styling**: Tailwind CSS (Premium Dark Mode support)\n-   **Data Visuals**: Framer Motion (Animations), SVG-based gauges\n-   **Communication**: Axios\n\n---\n\n## 📦 Installation & Setup\n\n### 1. Prerequisites\n-   Python 3.10+\n-   Node.js 18+\n-   MongoDB Atlas Cluster\n-   Google Gemini API Key\n\n### 2. Backend Setup\n```bash\ncd BackEnd\npython -m venv venv\nvenv\\Scripts\\activate  # Windows\npip install -r requirements.txt\n\n# Configure .env\n# MONGO_URI=your_mongodb_uri\n# GEMINI_API_KEY=your_gemini_key\n\npython app.py\n```\n\n### 3. Frontend Setup\n```bash\ncd FrontEnd\nnpm install\nnpm run dev\n```\n\n---\n\n## 📊 API Reference\n\n| Endpoint | Method | Description |\n| :--- | :--- | :--- |\n| `\u002Fapi\u002Fpredict` | `POST` | Ensemble risk prediction + SHAP feature importance. |\n| `\u002Fapi\u002Fexplain-ai` | `POST` | Generate Gemini-powered clinical interpretations. |\n| `\u002Fapi\u002Fchat` | `POST` | Interactive health assistant session. |\n| `\u002Fapi\u002Fresults` | `GET` | Retrieve model benchmark & evaluation data. |\n| `\u002Fapi\u002Fpatient\u002Fsave`| `POST` | Persist patient profile and history to MongoDB. |\n\n---\n\n## 🎨 Design System\nCardioIntel features a custom-built premium design system:\n- **Logo**: Pure SVG implementation with CSS pulse animations.\n- **Color Palette**: Deep Indigo, Emerald Success, and Rose Danger for clear clinical signaling.\n- **Glassmorphism**: Modern UI layers with backdrop blurs and subtle drop shadows.\n\n---\n\n## 📝 License\nThis project is licensed under the **MIT License**.\n\n---\n**Disclaimer**: *CardioIntel AI is intended for educational and clinical support research only. It is NOT a substitute for professional medical advice, diagnosis, or treatment. Always consult with a licensed healthcare professional.*\n","CardioIntel AI 是一个先进的全栈医疗智能平台，专注于高精度的心脏病风险评估。该项目结合了随机森林、XGBoost 和 LightGBM 等多种机器学习模型，并利用 Google Gemini 的生成能力，将原始临床数据转化为可操作的患者健康洞察。其核心功能包括通过集成 SHAP 和 LIME 实现的可解释性AI、实时可视化风险概率以及自动化的临床总结生成。此外，它还提供了一个基于上下文的聊天助手，能够根据用户的临床历史提供个性化的健康建议。CardioIntel 适合用于需要提高心脏病诊断准确性和为患者提供个性化健康管理方案的医疗机构或个人使用。",2,"2026-06-11 04:03:50","CREATED_QUERY"]