Which algorithms are used in this sleep health classification project?

The system implements and compares two algorithms: XGBoost and L1-penalized Logistic Regression. XGBoost achieved the best performance with 92.5% accuracy and an F1-score of 0.924.

What is the accuracy of this sleep health classification model?

The XGBoost model achieves 92.5% test accuracy with precision of 92.8%, recall of 92.5%, F1-score of 0.924, and ROC-AUC of 0.921, making it highly reliable for sleep disorder screening.

Who can benefit from this sleep health classification system?

Healthcare providers can use it for preliminary screening, occupational health programs for workplace wellness, telehealth platforms for accessible screening, and individuals for self-assessment of sleep health risks.

What technologies are used to build this project?

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

SleepWell AI - Intelligent Sleep Health & Lifestyle Classification System - Complete Project Demo Video

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Machine Learning

SleepWell AI - Intelligent Sleep Health & Lifestyle Classification System

SleepWell AI - Intelligent Sleep Health & Lifestyle Classification System is a comprehensive machine learning system that predicts sleep disorders using lifestyle and health parameters including sleep duration, stress levels, and physical activity. 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 to achieve 92.5% accuracy.

The system leverages 13 features including sleep duration, quality of sleep, stress level, physical activity level, daily steps, age, gender, occupation, BMI category, blood pressure, and heart rate. It provides interactive visualizations, feature importance analysis, model comparison, and real-time sleep disorder prediction to help healthcare professionals and individuals identify potential sleep disorders early.

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

Key Features:

Binary Sleep Disorder Classification
XGBoost (92.5% Accuracy)
L1 Logistic Regression (88.2% Accuracy)
SMOTE for Class Imbalance
Leak-Free Cross-Validation
Feature Importance Analysis
Real-time Health Prediction
Model Performance Comparison
5-Fold Stratified CV
Flask Web Application

Algorithms Used

⚡ XGBoost Classifier

Gradient boosting with 50 estimators, handles non-linear relationships effectively

🎯 Accuracy: 92.5%

📊 L1 Logistic Regression

L1-penalized logistic regression with automatic feature selection

🎯 Accuracy: 88.2%

🔄 SMOTE

Synthetic Minority Over-sampling for handling class imbalance

📊 k-neighbors: 5

🔍 5-Fold Leak-Free CV

Cross-validation with SMOTE inside each fold

📊 XGB: 90.2% ± 2.8%

Methodology & Workflow

1 Data Loading & Inspection
Sleep health dataset with 374 records and 13 features

2 Data Preprocessing
Cleaning, encoding, BP extraction, feature scaling

3 Feature Engineering
BP ratio, pulse pressure, sleep-activity index, stress-sleep ratio

4 Model Training
XGBoost & L1 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 sleep disorder prediction

Model Performance Comparison

Metric	XGBoost	L1 Logistic Regression	Best
Accuracy	0.925	0.882	XGBoost
Precision	0.928	0.876	XGBoost
Recall	0.925	0.882	XGBoost
F1-Score	0.924	0.878	XGBoost
ROC-AUC	0.921	0.856	XGBoost
CV Mean (5-Fold)	0.902	0.841	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (374 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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