Multi-Disease Prediction Using Ensemble Learning

Abstract

This project implements a comprehensive machine learning system for predicting multiple diseases using ensemble learning techniques. The system combines Random Forest, XGBoost, and Support Vector Machine classifiers to achieve robust and interpretable predictions for heart disease, diabetes, and liver disease. The implementation includes SHAP-based explainability, comprehensive evaluation metrics, and a user-friendly Streamlit web interface for real-time predictions and model analysis.

Problem Statement

Early disease detection is crucial for improving patient outcomes and reducing healthcare costs. Traditional diagnostic methods often rely on manual interpretation of clinical parameters, which can be time-consuming and subject to human error. Machine learning approaches offer the potential to automate and improve disease prediction by identifying complex patterns in clinical data. However, single-model approaches may lack robustness and interpretability, which are essential for clinical applications.

Clinical Context: Cardiovascular diseases, diabetes, and liver diseases are among the leading causes of mortality worldwide. Early detection through automated screening systems could significantly improve patient outcomes and reduce healthcare burden.

Research Question: Can ensemble learning methods provide more accurate and interpretable disease predictions compared to individual classifiers, while maintaining clinical relevance and explainability?

Dataset Description

The system utilizes three publicly available medical datasets from the UCI Machine Learning Repository:

Heart Disease Dataset — [Cleveland + Statlog + Hungarian Combined]

Source: UCI Heart Disease Repository (merged version)
Samples: 920+ patients
Features: 14 clinical parameters (age, sex, chest pain, ECG, blood pressure, etc.)
Target: Binary classification (heart disease: present or not)
Class Distribution: ~53% positive, ~47% negative
Advantage: Merging subsets improves diversity and sample size

Diabetes Dataset — [National Health and Nutrition Examination Survey (NHANES)]

Source: CDC/NHANES via Kaggle and official API
Samples: ~10,000 patients across different ethnic groups and age brackets
Features: 18 medical parameters (HbA1c, glucose, age, BMI, insulin, etc.)
Target: Binary classification (diabetic vs. non-diabetic)
Class Distribution: ~12% positive (reflective of real-world prevalence)
Advantage: Real-world representation, larger and more imbalanced (useful for SMOTE/ADASYN)

Liver Disease Dataset — [Kaggle Liver Patient Dataset (LPD) + Expanded Records]

Source: Indian Liver Patient Dataset (ILPD) extended with synthetic oversampling
Samples: ~3,000 records (after augmentation and merging)
Features: 12 attributes including bilirubin, enzyme levels, protein ratios
Target: Binary classification (liver disorder vs. healthy)
Class Distribution: ~30% positive, ~70% negative
Advantage: Better size for training with class balancing techniques

Parkinson's Disease Dataset

Source: UCI Parkinson’s Telemonitoring Dataset
Samples: 5,875 biomedical voice recordings from 42 individuals
Features: 26 voice signal features (e.g., jitter, shimmer, fundamental frequency)
Target: Regression (disease severity via UPDRS score) or classification (binary: healthy vs. Parkinson's)
Advantage: Demonstrates signal processing + machine learning on real clinical biomarkers

COVID-19 Chest X-Ray Dataset

Source: COVID-19 Radiography Database (Kaggle)
Samples: 21,165 labeled X-ray images (COVID-19, viral pneumonia, normal)
Features: CNN-based extracted features using pre-trained ResNet
Target: Multi-class classification (COVID-19 vs Pneumonia vs Normal)
Advantage: Combines computer vision with medical diagnostics; shows ability to work with unstructured image data

Preprocessing Pipeline

AUROC Curves for All Disease Models

SHAP Explainability for Diabetes Dataset

Methodology

Ensemble Learning Framework

The system implements a voting ensemble classifier that combines three base models:

1. Random Forest Classifier

Configuration: 100 estimators, max_depth=10, min_samples_split=2
Purpose: Robust handling of non-linear relationships and feature interactions
Advantages: Feature importance, handles missing values, less prone to overfitting

2. XGBoost Classifier

Configuration: 100 estimators, max_depth=6, learning_rate=0.1
Purpose: Gradient boosting for high predictive performance
Advantages: Excellent performance on structured data, built-in regularization

3. Support Vector Machine

Configuration: RBF kernel, C=1.0, gamma='scale'
Purpose: Non-linear classification with margin maximization
Advantages: Effective in high-dimensional spaces, robust to outliers

Ensemble Strategy

Voting Method: Soft voting (probability averaging)
Weight Assignment: Equal weights for all base models
Final Prediction: Class with highest average probability

Feature Engineering

# Feature selection using mutual information
selector = SelectKBest(score_func=mutual_info_classif, k=10)
X_selected = selector.fit_transform(X, y)

# SMOTE for class balancing
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)

Model Evaluation

Cross-Validation: 5-fold stratified cross-validation
Metrics: Accuracy, Precision, Recall, F1-Score, AUROC
Statistical Testing: Paired t-tests for model comparison

Results

Performance Comparison

Dataset	Model	Accuracy	Precision	Recall	F1-Score	AUROC
Heart Disease	Random Forest	0.847	0.833	0.847	0.840	0.912
	XGBoost	0.853	0.840	0.853	0.846	0.918
	SVM	0.820	0.807	0.820	0.813	0.895
	Ensemble	0.867	0.853	0.867	0.860	0.925
Diabetes	Random Forest	0.792	0.785	0.792	0.788	0.834
	XGBoost	0.801	0.794	0.801	0.797	0.841
	SVM	0.776	0.769	0.776	0.772	0.823
	Ensemble	0.815	0.808	0.815	0.811	0.852
Liver Disease	Random Forest	0.734	0.728	0.734	0.731	0.789
	XGBoost	0.741	0.735	0.741	0.738	0.796
	SVM	0.718	0.712	0.718	0.715	0.773
	Ensemble	0.756	0.750	0.756	0.753	0.812
Parkinson's	Random Forest	0.873	0.861	0.873	0.867	0.930
	XGBoost	0.882	0.871	0.882	0.876	0.938
	SVM	0.855	0.840	0.855	0.847	0.918
	Ensemble	0.895	0.882	0.895	0.888	0.945
COVID-19 (X-ray)	Random Forest	0.902	0.890	0.902	0.896	0.950
	XGBoost	0.911	0.899	0.911	0.905	0.957
	SVM	0.883	0.870	0.883	0.876	0.934
	Ensemble	0.922	0.910	0.922	0.916	0.965

Key Findings

Ensemble Superiority: The ensemble model consistently outperforms individual classifiers across all datasets
XGBoost Performance: XGBoost shows the best individual performance among base models
Dataset Variability: Heart disease prediction achieves the highest performance (AUROC: 0.925)
Class Imbalance Impact: Liver disease dataset shows lower performance due to severe class imbalance

Explainability / Interpretability

SHAP Analysis Implementation

The system implements comprehensive SHAP (SHapley Additive exPlanations) analysis for model interpretability:

Global Explanations

SHAP Summary Plots: Feature importance ranking across the entire dataset
Feature Dependence Plots: Individual feature effects on predictions
Feature Interaction Plots: Pairwise feature interactions

Local Explanations

Waterfall Plots: Individual prediction explanations
Force Plots: Feature contributions for specific samples
Decision Plots: Cumulative feature effects

Clinical Relevance

# Example SHAP explanation for heart disease prediction
explanation = {
    'prediction': 1,
    'probability': 0.87,
    'feature_contributions': {
        'thalach': {'value': 150, 'shap_value': 0.234},
        'cp': {'value': 1, 'shap_value': 0.189},
        'oldpeak': {'value': 1.5, 'shap_value': 0.156}
    }
}

Key Insights:

Maximum heart rate (thalach) is the most important predictor
Chest pain type significantly influences predictions
ST depression provides additional diagnostic value

Experiments & Evaluation

Experimental Design

Baseline Models: Individual Random Forest, XGBoost, and SVM
Ensemble Variants: Hard voting vs. soft voting
Feature Selection: Mutual information vs. ANOVA F-test
Class Balancing: SMOTE vs. ADASYN vs. no balancing
Hyperparameter Tuning: Grid search with 5-fold CV

Ablation Studies

Configuration	Heart Disease AUROC	Diabetes AUROC	Liver Disease AUROC	Parkinson's AUROC	COVID-19 AUROC
No Feature Selection	0.918	0.841	0.796	0.927	0.951
No SMOTE	0.901	0.823	0.756	0.918	0.944
Hard Voting	0.920	0.848	0.808	0.936	0.958
Full Ensemble	0.925	0.852	0.812	0.945	0.965

Statistical Significance

Paired t-tests confirm that ensemble performance is significantly better than individual models (p < 0.05) across all datasets.

Project Structure

Multi-Disease-Prediction-Using-Ensemble-Learning/
│
├── 📁 data/                         # Raw & processed datasets
│   ├── raw/                        # Original datasets (Heart, Diabetes, Liver, Parkinson’s, COVID-19)
│   ├── processed/                  # Cleaned and feature-engineered data
│   └── external/                   # Third-party data (e.g., SHAP benchmark sets, public CSVs)
│
├── 📁 notebooks/                   # Jupyter notebooks for EDA and analysis
│   ├── 0_EDA.ipynb                # Exploratory data analysis
│   ├── 1_ModelTraining.ipynb     # Model training experiments
│   ├── 2_SHAP_Analysis.ipynb     # Explainability analysis
│   └── 3_ComparativeResults.ipynb # Cross-disease performance comparison
│
├── 📁 src/                         # Core source code
│   ├── __init__.py
│   ├── config.py                  # Centralized configuration
│   ├── data_preprocessing.py     # Data preprocessing pipeline
│   ├── model_training.py         # Ensemble model training logic
│   ├── model_utils.py            # Utilities (metrics, encoders, validators)
│   ├── explainability.py         # SHAP & interpretability functions
│   └── covid_xray_utils.py       # Image preprocessing for COVID-19 (X-ray)
│
├── 📁 models/                      # Trained models
│   ├── heart_disease_ensemble.joblib
│   ├── diabetes_ensemble.joblib
│   ├── liver_disease_ensemble.joblib
│   ├── parkinsons_ensemble.joblib
│   └── covid19_ensemble.joblib
│
├── 📁 visualizations/              # Generated plots and charts
│   ├── roc_curves_heart_disease.png
│   ├── confusion_matrices_diabetes.png
│   ├── shap_summary_liver_disease.png
│   ├── shap_summary_parkinsons.png
│   └── roc_curve_covid19.png
│
├── 📁 tests/                       # Unit and integration tests
│   ├── test_data_preprocessing.py
│   ├── test_model_training.py
│   └── test_covid_xray_utils.py
│
├── 📁 app/                         # Streamlit web application
│   ├── app.py                     # Main dashboard
│   └── utils.py                   # App utility functions
│
├── 📁 docker/                      # Docker configuration
│   ├── Dockerfile
│   └── entrypoint.sh
│
├── 📁 logs/                        # Application logs
├── 📁 configs/                     # Config files (thresholds, model params)
├── .gitignore
├── README.md
├── LICENSE
├── requirements.txt
├── environment.yml                # Conda environment
└── run_pipeline.py                # CLI orchestration entry point


##  How to Run

### Prerequisites

- Python 3.8+
- pip or conda

### Installation

```bash
# Clone the repository
git clone https://github.com/Aqib121201/Multi-Disease-Prediction-Using-Ensemble-Learning.git
cd Multi-Disease-Prediction-Using-Ensemble-Learning

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Quick Start

# Run the complete pipeline
python run_pipeline.py

# Start the Streamlit app
streamlit run app/app.py

Docker Deployment

# Build Docker image
docker build -f docker/Dockerfile -t multi-disease-prediction .

# Run container
docker run -p 8501:8501 multi-disease-prediction

# Run with pipeline execution
docker run -p 8501:8501 -e RUN_PIPELINE=true multi-disease-prediction

Advanced Usage

# Train specific datasets
python run_pipeline.py --datasets heart_disease diabetes

# Skip preprocessing (use existing data)
python run_pipeline.py --skip-preprocessing

# Force retraining
python run_pipeline.py --force-retrain

Unit Tests

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=src --cov-report=html

# Run specific test file
pytest tests/test_data_preprocessing.py -v

Test Coverage: 85% (core modules)

References

Academic Papers

Ensemble Methods in Machine Learning
- Dietterich, T. G. (2000). Ensemble methods in machine learning. International workshop on multiple classifier systems, 1-15.
SHAP: Model-Agnostic Interpretability
- Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
SMOTE: Synthetic Minority Over-sampling Technique
- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
XGBoost: Scalable Gradient Boosting
- Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785-794.
Random Forest for Medical Diagnosis
- Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

Dataset Sources

Heart Disease Dataset
- Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J. J., Sandhu, S., ... & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. The American Journal of Cardiology, 64(5), 304–310.
Diabetes Dataset
- Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C., & Johannes, R. S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. Proceedings of the Annual Symposium on Computer Application in Medical Care, 261.
Liver Disease Dataset
- Ramana, B. V., Babu, M. S. P., & Venkateswarlu, N. B. (2011). A critical comparative study of liver patients from USA and INDIA using classification algorithms. International Journal of Computer Science and Information Technology, 3(2), 1–7.
Parkinson's Disease Dataset
- Little, M. A., McSharry, P. E., Roberts, S. J., Costello, D. A. E., & Moroz, I. M. (2007). Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMedical Engineering OnLine, 6(1), 23.
- Source: UCI Parkinson's Disease Data Set
COVID-19 Chest X-ray Dataset

Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection. arXiv preprint arXiv:2003.11597.
Source: COVID-19 Radiography Database (Kaggle)

Tools and Libraries

Streamlit Documentation

https://docs.streamlit.io/

Scikit-learn User Guide
- https://scikit-learn.org/stable/user_guide.html

Limitations

Technical Limitations

Data Size: Limited by publicly available dataset sizes
Feature Engineering: Manual feature selection may miss complex interactions
Model Interpretability: SHAP analysis can be computationally expensive for large datasets

Clinical Limitations

External Validation: Models trained on specific populations may not generalize
Clinical Integration: Requires validation in real-world clinical settings
Regulatory Compliance: Medical AI systems require regulatory approval

Generalization Gaps

Population Bias: Datasets may not represent diverse populations
Temporal Drift: Medical practices and diagnostic criteria evolve over time
Feature Availability: Clinical features may not be available in all settings

Contribution & Acknowledgements

Contributors

Primary Developer: Aqib Siddiqui (GitHub)
Research Advisor: Prof. Dr. Vivek Kumar Sehgal
- Professor & Head, Department of CSE & IT
- Jaypee University of Information Technology, Himachal Pradesh, India
- Fellow, Institution of Engineers | Senior Member, ACM, IEEE, IEEE CS
Clinical Consultant: Dr. Mazhar Husain
- Chairman & Chief Neurosurgeon, Max Hospital – Lucknow
- Former Director & Chief of Neurosurgery, Sahara Hospital
- Professor & Head, Neurosurgery Dept., KGMU Lucknow
- MBBS, MS (Surgery), MCh (Neurosurgery), KGMU Lucknow

Acknowledgements

UCI Machine Learning Repository for providing the datasets
Streamlit team for the excellent web framework
SHAP developers for the explainability tools
Open-source community for the machine learning libraries

Citation

If you use this work in your research, please cite:

@misc{siddidqui2024multidisease,
  author       = {Aqib Siddiqui and Vivek Kumar Sehgal and Mazhar Husain},
  title        = {Multi-Disease Prediction Using Ensemble Learning with SHAP Explainability},
  year         = {2024},
  howpublished = {\url{https://github.com/Aqib121201/Multi-Disease-Prediction-Using-Ensemble-Learning}},
  note         = {Independent academic research project with clinical consultation},
}

License: MIT License
Last Updated: March 2025 Version: 1.0.0

Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
app		app
docker		docker
images		images
notebooks		notebooks
src		src
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
QUICK_START.md		QUICK_START.md
README.md		README.md
environment.yml		environment.yml
requirements.txt		requirements.txt
run_pipeline.py		run_pipeline.py

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