Early Detection of Cardiovascular Abnormalities using ECG Data

Abstract

This study examines the utilization of machine learning algorithms for the early identification of cardiovascular illnesses through electrocardiogram (ECG) data. The study assesses various models, including Support Vector Machines (SVM), Logistic Regression (LR), NB, Linear Regression (LinReg), and ensemble methods such as XGBoost, RF, Gradient Boosting (GB, and KNN. The dataset obtained from Kaggle contains ECG measurements and binary labels denoting normal or pathological cardiac function. The performance of each algorithm is evaluated according to its proficiency in accurately classifying ECG patterns and differentiating between normal and pathological cardiac activity. The findings indicate that SVM attains the best accuracy of 0.994, illustrating its proficiency in identifying intricate, non-linear correlations within high-dimensional ECG data. LR and RF closely follow with accuracies of 0.993, showcasing their robustness in modeling linear and probabilistic trends. LinReg performs admirably with an accuracy of 0.992, while XGBoost and KNN achieve comparable scores of 0.9904, highlighting their versatility and noise tolerance. GB and NB report slightly lower accuracies of 0.9888 and 0.9688, respectively, yet remain valuable due to their unique strengths in handling diverse data distributions and probabilistic classification. Ensemble techniques like XGBoost, RF and GB leverage the power of multiple weak learners to deliver strong predictive performance. Meanwhile, KNN's adaptability to varying data patterns underscores its utility in practical applications. These findings highlight the capability of machine learning algorithms to automate ECG interpretation and aid healthcare professionals in prompt diagnosis. Future work should focus on optimizing these models further and validating their performance in real-world clinical settings. By enhancing diagnostic accuracy and efficiency, this research contributes to advancing cardiovascular health monitoring and improving patient outcomes.