Optimized Machine Learning Approaches for Better Sleep Disorder Diagnosis

Sleep disorders, particularly sleep apnea, are a significant health concern affecting millions of individuals globally. Timely and accurate detection of sleep apnea is essential for early intervention and effective treatment. This project presents an optimized machine learning-based approach for detecting sleep apnea using electrocardiogram (ECG) data. The system employs three powerful machine learning algorithms—Support Vector Machine (SVM), Random Forest, and XGBoost—to classify ECG signals into two categories: normal (0) and sleep apnea (1). By utilizing these models, the system aims to provide accurate, real-time detection of sleep apnea from ECG signals, which are commonly used for diagnosing this disorder. The models are trained and evaluated using Python, leveraging popular libraries such as scikit-learn for SVM, Random Forest, and XGBoost implementations. The goal is to create a robust and scalable solution that can assist healthcare professionals in diagnosing sleep apnea efficiently, ultimately improving patient outcomes. This work emphasizes the potential of machine learning in enhancing diagnostic accuracy and providing automated support for healthcare providers.

Keywords: Sleep Apnea, ECG Data, Machine Learning, Support Vector Machine (SVM), Random Forest, XGBoost, Classification, Healthcare, Diagnostic System, Real-time Detection, Python, Early Diagnosis.