Enhancing Arrhythmia Classification using Machine Learning on High-Dimensional EHR Data

Our research explores the application of advanced machine learning algorithms in conjunction with Principal Component Analysis (PCA) to enhance the classification of arrhythmia using electronic health records (EHRs). Arrhythmia, a condition marked by irregular heart rhythms, poses significant risks if left undiagnosed or improperly managed. The dataset utilized, named data arrhythmia, consists of a diverse and high-dimensional array of patient information, encompassing demographic details, clinical measurements, and diagnostic indicators. The inherent high dimensionality of the dataset presents both a challenge and an opportunity, as it is well-suited for showcasing the critical role of dimensionality reduction techniques. By applying PCA, the project aims to reduce the complexity of the dataset by identifying principal components that capture the most significant variance. This transformation simplifies the data, retaining its core informational content while mitigating issues like overfitting and computational inefficiency that often arise with high-dimensional datasets. The smaller dataset is subsequently employed to train a range of machine learning models, facilitating precise classification of arrhythmias and the effective assessment of patient risk.