Skip to content

TimothyLabs/neoguard_ai

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

124 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

NeoGuard AI - Neonatal Risk Early Warning System

Overview

NeoGuard AI is a machine learning-powered clinical decision support tool designed to predict neonatal risk using maternal, socioeconomic, and birth-related factors.

It is built to support:

  • Primary Health Care (PHC) workers
  • Hospitals and clinicians
  • Public health analysts

The goal is to enable early detection of high-risk neonates and reduce preventable neonatal mortality.

Problem Statement

Neonatal mortality remains a major global health challenge, especially in low- and middle-income countries like Nigeria. Many high-risk cases are:

  • Detected late
  • Poorly monitored
  • Not referred in time

This leads to preventable deaths.

Solution

NeoGuard AI provides:

  • Real-time risk prediction
  • Clinical recommendations
  • Population-level insights
  • Early warning alerts

How It Works

  1. Input Data: The model uses key predictors such as:Maternal ageAntenatal care visitsBirth intervalBirth weightDelivery locationSocioeconomic indicators
  2. Processing: Data is cleaned and encodedFeatures aligned with trained model
  3. Prediction: Outputs risk probability (0–1)
  4. Classifies into:- Low Risk
    • Moderate Risk
    • High Risk
  5. Output:- Clinical recommendations
    • Key risk factor explanations
    • Downloadable PDF reports
    • Batch analysis for multiple patients

NeoGuard Features

Clinical Risk Assessment

  • Manual patient input
  • Instant prediction
  • Actionable recommendations

Batch Analysis (CSV Upload)

  • Analyze multiple patients
  • Download results
  • Identify high-risk groups

Population Dashboard

  • Risk distribution visualization

Early Warning Alerts

  • Detects spikes in high-risk cases
  • Generates actionable alerts

Dataset

This project is based on data inspired by the Demographic and Health Surveys (DHS).

DHS Reference

The DHS Program provides nationally representative data at https://dhsprogram.com

Tech Stack

  • Python
  • Streamlit (Frontend)
  • Scikit-learn (Modeling)
  • Pandas / NumPy (Data processing)
  • Plotly (Visualization)
  • ReportLab (PDF generation)

Model Classification Model Output: Probability of neonatal risk

Thresholds:

  • ≥ 0.7 → High Risk
  • 0.4–0.69 → Moderate Risk
  • < 0.4 → Low Risk

Installation

git clone https://github.com/yourusername/neoguard_ai.git

cd neoguard-ai

pip install -r requirements.txt streamlit

run app.py

Usage

Manual Mode

  • Enter patient details
  • Click Predict Risk

CSV Mode

  • Upload template
  • View batch predictions
  • Download results

 Output Example

  • Risk classification
  • Probability score
  • Clinical advice
  • Risk factor explanation
  • PDF report

 Limitations NeoGuard is not a substitute for clinical judgment. It requires validation on real-world hospital dataModel performance depends on data quality

Future Improvements

  • Integration with hospital EMR systems
  • Real-time national surveillance dashboard
  • Explainable AI
  • Mobile app deployment

Live app: https://neoguardai.streamlit.app/

About

AI-powered neonatal risk prediction and early warning system for clinical decision support and national health surveillance.

Topics

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

No releases published

Packages

 
 
 

Contributors