Which algorithms are used in this fetal health classification project?

The system implements and compares two classification algorithms: XGBoost and Logistic Regression. XGBoost achieved the best performance with 93.8% accuracy and a weighted F1-score of 0.937.

What is the accuracy of this fetal health classification model?

The XGBoost model achieves 93.8% test accuracy with a precision of 93.6%, recall of 93.8%, F1-score of 0.937, and ROC-AUC of 0.974, making it highly reliable for clinical decision support.

Who can benefit from this fetal health classification system?

Obstetricians, midwives, and healthcare professionals in prenatal care can use it for clinical decision support. Hospitals can improve fetal monitoring quality and reduce inter-observer variability. It's also valuable for telemedicine and remote monitoring applications.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, and Seaborn for machine learning. SMOTE is used for handling class imbalance. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

FetalGuard: Intelligent Fetal Health Classification Using Machine Learning

FetalGuard: Intelligent Fetal Health Classification Using Machine Learning is a comprehensive machine learning system that automatically classifies fetal health status from cardiotocography (CTG) data into three categories: Normal, Suspect, and Pathological. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like XGBoost and Logistic Regression with SMOTE for class imbalance to achieve 93.8% accuracy.

The system leverages 22 clinically significant features extracted from CTG signals including baseline FHR, short-term variability, long-term variability, accelerations, decelerations, uterine contractions, and histogram statistics. It provides interactive visualizations, feature importance analysis, model comparison, and real-time fetal health classification to help healthcare professionals make faster, more accurate assessments.

Python 3.8+ Machine Learning XGBoost Logistic Regression SMOTE Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class Fetal Health Classification
XGBoost (93.8% Accuracy)
Logistic Regression (91.2% Accuracy)
SMOTE for Class Imbalance
Leak-Free Cross-Validation
Feature Importance Analysis
Real-time Health Prediction
Model Performance Comparison
Interactive Visualizations
Flask Web Application

Algorithms Used

⚡ XGBoost Classifier

Gradient boosting with 200 estimators, handles non-linear relationships and class imbalance

🎯 Accuracy: 93.8%

📊 Logistic Regression

Multinomial regression with balanced class weights, interpretable coefficients

🎯 Accuracy: 91.2%

🔄 SMOTE

Synthetic Minority Over-sampling Technique for handling class imbalance

📊 F1-Score: 0.937

🔍 5-Fold Leak-Free CV

Cross-validation with SMOTE inside each fold

📊 CV Mean: 0.941 ± 0.011

Methodology & Workflow

1 Data Loading & Inspection
CTG dataset with 2,126 records and 22 features

2 Data Preprocessing
Cleaning, outlier removal, median imputation

3 Feature Engineering
Variability ratios, total decelerations, histogram CV

4 Model Training
XGBoost & Logistic Regression with SMOTE pipelines

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 5-fold CV

6 Web Deployment
Flask web app for real-time fetal health classification

Model Performance Comparison

Metric	XGBoost	Logistic Regression	Best
Accuracy	0.938	0.912	XGBoost
Precision (Weighted)	0.936	0.909	XGBoost
Recall (Weighted)	0.938	0.912	XGBoost
F1-Score (Weighted)	0.937	0.910	XGBoost
ROC-AUC (Weighted)	0.974	0.953	XGBoost
CV Mean (5-Fold)	0.941	0.908	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (2,126 CTG records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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Complete Source Code

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