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🏥 ML Prediction of Acute Respiratory Infections (ARI) in Bangladesh

Rural vs. Urban Analysis for Under-Five Children

📌 Overview

This project applies machine learning (ML) techniques to predict Acute Respiratory Infections (ARI) among children under five in Bangladesh, comparing urban and rural areas. ARI remains a leading cause of child mortality, and early detection can help policymakers and healthcare providers take preventive measures.

📂 Dataset

  • Source: ARI dataset from Bangladesh
  • Format: CSV
  • Key Features:
    • Demographic variables (age, gender, household location)
    • Socioeconomic factors (parental education, income levels)
    • Environmental factors (air quality, household cooking fuel)
    • Health indicators (previous infections, vaccination status)
    • Target Variable: ARI (0 = No ARI, 1 = ARI Present)

🏗️ Methodology

🔍 Data Preprocessing

  • Handling missing values
  • Encoding categorical variables
  • Feature scaling using StandardScaler
  • Splitting data into training & testing sets

📊 Exploratory Data Analysis (EDA)

  • Class distribution analysis
  • Correlation heatmaps
  • Feature importance analysis
  • Urban vs. Rural ARI prevalence comparison

🔧 Machine Learning Models

The following models were implemented and evaluated:

Model Description
Logistic Regression Baseline model for classification
Random Forest Handles non-linearity and feature importance
Gradient Boosting (XGBoost) Advanced tree-based method for better accuracy
Neural Networks Deep learning approach for complex feature interactions

🏆 Model Evaluation

  • Accuracy, Precision, Recall, F1-Score
  • ROC-AUC Curve for performance comparison
  • Feature importance ranking

🚀 Installation & Usage

🔥 Prerequisites

Ensure you have the following installed:

  • Python 3.8+
  • Pandas, NumPy, Matplotlib, Seaborn
  • Scikit-learn, XGBoost, TensorFlow

📥 Clone Repository

git clone https://github.com/your-username/ML-Prediction-of-Acute-Respiratory-Infections.git
cd ML-Prediction-of-Acute-Respiratory-Infections