Electro Cardio Gram Using Different Machine Learning Techniques for Early Heart Attack Prediction
Abstract
An electrocardiogram (ECG) is used to monitor the heart's electric impulses and visualize cardiac signals in order to identify issues. For the early identification of heart-related conditions, the non-invasive Electrocardiogram (ECG), which offers data on cardiac abnormalities, has become a common procedure. A variety of methods are employed to identify irregular heartbeats. To predict cardiovascular illnesses, which can lead to severe illness or even death in middle-aged and older persons, this study suggests a way to categorize ECG records. One of the largest anomalies was caused by an arrhythmia sickness.
Several deep learning approaches were utilized to predict early arrhythmias and save lives. Several ECG signal classifications have been done utilizing pre-existing databases, such as MIT-BIH arrhythmia, according to a review of the literature. In order to detect irregularities associated with arrhythmias.
This research suggests an architecture that integrates a number of heart disease classification methods with a 99.7% accuracy rate, including logistic regression, CNN, LSTM, decision trees, k-nearest neighbors, Naive Bayes, discriminant analysis, and neural networks.
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Amit Gupta, Arun Singh Chouhan, Kanagaraj Jyothi,Abha Dargar, Shashi Kant Dargar,”An Effective Investigation on Implementation of Different Learning Technique Used For Heart Disease Prediction”, 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future ons)(ICRITO),https://ieeexplore.ieee.org/document/10522150.
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Copyright (c) 2025 K. Kishore Kumar, G. Suneetha, K.Chandrika Reddy, P.Srinivasa Rao, Amit Gupta
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