diabetic-readmission-prediction

Predicting diabetic patient hospital readmission using ensemble machine learning models.

Diabetic Patient Readmission Prediction

Overview

Hospital readmission among diabetic patients is a major healthcare challenge, contributing to increased medical costs and poorer patient outcomes. This project focuses on building a machine learning–based predictive system to identify diabetic patients at risk of hospital readmission using clinical and demographic data.

The goal is to support data-driven decision-making in healthcare by enabling early intervention for high-risk patients.

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Problem Statement

Unplanned hospital readmissions of diabetic patients place a significant burden on healthcare systems. Traditional rule-based approaches often fail to capture complex interactions among patient features.

This project addresses the problem by:

• Modeling hospital readmission as a binary classification task
• Leveraging ensemble learning techniques to improve predictive performance
• Handling class imbalance, a common issue in medical datasets

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Dataset

The dataset used in this project contains records of diabetic patients, including:

• Demographic information
• Clinical measurements and diagnoses
• Hospital admission history

Note: The dataset has been preprocessed to handle missing values, categorical variables, and class imbalance.

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Methodology

The project follows a structured machine learning pipeline:

1. Data Preprocessing

   • Handling missing values
   • Encoding categorical features
   • Feature selection
   • Addressing class imbalance using resampling techniques

2. Model Development

   • Baseline machine learning models

   • Ensemble learning approaches, including:

     • Random Forest
     • Boosting-based models
     • Stacked ensemble models

3. Evaluation

   • Model performance assessed using:

     • Accuracy
     • Precision
     • Recall
     • F1-score
     • ROC–AUC

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Results

Ensemble models demonstrated improved predictive performance compared to individual base learners, particularly in identifying patients at high risk of readmission. This highlights the effectiveness of ensemble learning in healthcare prediction tasks involving complex and imbalanced data.

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Tools & Technologies

• Programming Language: Python
• Libraries: pandas, NumPy, scikit-learn, XGBoost, CatBoost
• Environment: Jupyter Notebook / Google Colab

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Project Structure

diabetic-readmission-prediction/
│
├── notebooks/        # Data analysis and model development
├── data/             # Dataset 
├── results/          # Model evaluation outputs
├── README.md
└── requirements.txt

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Conclusion

This project demonstrates how ensemble machine learning techniques can be effectively applied to predict hospital readmission among diabetic patients. The approach has the potential to assist healthcare providers in identifying high-risk cases early and improving patient management strategies.

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