| --- |
| 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. |
|
|
