Impact of ECG Signal Processing Techniques on Machine Learning Accuracy
Machine Learning Course Research Project
Researched the impact of preprocessing and feature extraction techniques on ML model performance for ECG-based heart disease detection, aiming to enhance diagnostic accuracy and automated healthcare outcomes
Project Overview
This project aims to explore how various ECG signal processing techniques affect the accuracy of machine learning models for heart disease classification. The primary focus is on evaluating the impact of preprocessing techniques on the quality of feature extraction and how these influence the overall performance of machine learning algorithms used for ECG signal classification. This research aims to improve ECG signal classification by fine-tuning the preprocessing and feature extraction processes. By optimizing machine learning models, the project has the potential to enhance diagnostic capabilities, contributing to more accurate heart disease detection and better patient outcomes in automated healthcare systems.