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README.md

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+---
+language:
+- en
+license: mit
+library_name: sklearn
+tags:
+- tabular-classification
+- healthcare
+- medical
+- disease-prediction
+- ensemble
+- scikit-learn
+metrics:
+- accuracy
+model-index:
+- name: AI-Driven Healthcare Disease Prediction Ensemble
+  results:
+  - task:
+      type: tabular-classification
+      name: Disease Classification
+    metrics:
+    - type: accuracy
+      value: 0.1333
+      name: Test Accuracy
+---
+
+# 🩺 AI-Driven Healthcare Disease Prediction Ensemble
+
+A multi-model ensemble classifier built with **Scikit-Learn** combining **Random Forest**, **Support Vector Machines (RBF Kernel)**, and **Gaussian Naive Bayes** to predict clinical pathologies from patient symptom indicators.
+
+## 🚀 Model Details
+- **Architecture**: `VotingClassifier` with soft probability voting
+- **Sub-Estimators**:
+  - `RandomForestClassifier` (100 estimators, Gini criterion) — Weight: `0.40`
+  - `SVC` (Radial Basis Function Kernel, probability=True) — Weight: `0.35`
+  - `GaussianNB` (Multinomial Prior Likelihood) — Weight: `0.25`
+- **Number of Symptom Features**: 180 binary indicators (0 = absent, 1 = present)
+- **Number of Disease Classes**: 10 target pathologies
+- **Test Accuracy**: **13.33%** on independent clinical testing cohort
+
+---
+
+## 📦 Files in this Repository
+| File | Description |
+| :--- | :--- |
+| **`model.joblib`** | The complete trained `VotingClassifier` ensemble pipeline. |
+| **`symptoms.json`** | Array of 180 symptom feature names in exact column sequence. |
+| **`diseases.json`** | List of all 10 prognostic disease labels. |
+| **`prescriptions.json`** | Clinical treatment and prescription mapping for predicted pathologies. |
+
+---
+
+## 💻 Quickstart Inference (Python)
+
+```python
+import joblib
+import json
+import numpy as np
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# 1. Download model and feature list from Hugging Face
+REPO_ID = "YOUR_USERNAME/YOUR_MODEL_NAME"  # e.g., "Av1447/disease-prediction-ensemble"
+
+model_path = hf_hub_download(repo_id=REPO_ID, filename="model.joblib")
+symptoms_path = hf_hub_download(repo_id=REPO_ID, filename="symptoms.json")
+
+model = joblib.load(model_path)
+with open(symptoms_path, "r") as f:
+    symptom_features = json.load(f)
+
+# 2. Build input vector for patient symptoms
+# Example: Patient has Headache and Sensitivity to Light
+patient_symptoms = ["Headache", "Sensitivity to Light", "Sensitivity to Sound"]
+
+# Binary vector (1 if symptom is present, 0 otherwise)
+input_vector = [1 if feature in patient_symptoms else 0 for feature in symptom_features]
+input_df = pd.DataFrame([input_vector], columns=symptom_features)
+
+# 3. Predict disease and probability
+probabilities = model.predict_proba(input_df)[0]
+top_idx = probabilities.argmax()
+predicted_disease = model.classes_[top_idx]
+confidence = probabilities[top_idx] * 100
+
+print(f"Predicted Diagnosis: {predicted_disease} ({confidence:.1f}% confidence)")
+```
+
+---
+
+## 🌐 Serverless Inference API / Web Usage
+You can query this model directly via Hugging Face Serverless Inference API or via client-side web applications hosted on Vercel or GitHub Pages.
+
+```javascript
+// Example JavaScript fetch from your frontend
+const response = await fetch(
+  "https://api-inference.huggingface.co/models/YOUR_USERNAME/YOUR_MODEL_NAME",
+  {
+    headers: {
+      Authorization: "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
+      "Content-Type": "application/json",
+    },
+    method: "POST",
+    body: JSON.stringify({ inputs: [inputVector] }),
+  }
+);
+const result = await response.json();
+```
+
+---
+
+## ⚖️ Intended Use & Disclaimer
+This model is developed for research, education, triage assistance, and prototyping. It is not intended to replace professional medical diagnosis, laboratory testing, or clinical consultation.

diseases.json

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+[
+  "Asthma",
+  "COVID-19",
+  "Cold",
+  "Diabetes",
+  "Flu",
+  "Hypertension",
+  "Malaria",
+  "Migraine",
+  "Pneumonia",
+  "Tuberculosis"
+]