About the project: The project focuses on leveraging machine learning and deep learning techniques to identify and classify arrhythmias within ECG data. By using the MIT-BIH Arrhythmia Database, we have analyzed a collection of ECG recordings and categorized them into five classes, including normal rhythms and various arrhythmias. Our work involves data preprocessing, feature engineering, and model training using artificial neural networks, recurrent neural networks (LSTM), and convolutional neural networks (CNN) as well as machine learning models such as rancom forest, support vector machines and XGBoost just to name a few. The project's ultimate goal is to enhance the accuracy of arrhythmia detection, contributing to advancements in healthcare and patient well-being.
About Disleve Kanku: I am a passionate data scientist with a strong commitment to leveraging data science for the betterment of healthcare. Currently pursuing my Master's in Data Science at Northeastern University, I've dedicated my academic and professional journey to the field of precision medicine and arrhythmia detection.
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