Analysis on Heart Arrhythmia prediction and Classification
DOI:
https://doi.org/10.61841/yxrre767Keywords:
Machine Learning, Artificial Intelligence, ECG, Heart Arrhythmia, Convolutional Neural Network, XGBoostAbstract
Machine Learning is an integral part of Artificial Intelligence, is a science of statistical models and their algorithms. It is used to train a model to perform a real-world task without explicit commands. Heart Arrhythmia is a life-threatening disease dealing with an irregular heartbeat. ECG signals are the most accurate measure to find out the functionality of the cardiovascular-system. We put forward a solution to solve the difficulty of choosing among the various state-of-art models for heart arrhythmia prediction. This helps the hospitals with less experienced doctors to more accurately predict the disease at a lower cost. The Convolutional Neural Network model we built is more accurate than the existing models. We have also analyzed with other machine learning models such as Logistic Regression, Random Forest and XGBoost for the hospitals with lesser computation power.
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