Key result
ANN model predicts heartbeat conditions with ~89% accuracy.
Why the study?
The alarming rate of heart defects and increasing death toll called for an urgent need to improve ECG platforms with enhanced machine learning techniques for more accurate predictive diagnosis of cardiovascular disorders.
Does an Artificial Neural Network (ANN) machine learning approach accurately predict normal and abnormal ECG heartbeat conditions in a dataset of 303 patients?
Does an Artificial Neural Network (ANN) machine learning approach accurately predict normal and abnormal ECG heartbeat conditions in a dataset of 303 patients?
Answers are grounded in this paper and cite their sources.
An Artificial Neural Network model applied to a 303-patient Kaggle heart dataset achieved 88.52% accuracy in classifying normal versus abnormal ECG heartbeats, demonstrating baseline feasibility for automated ECG analysis.
The vital role played by the heart for the sustenance of humanity is such that the heart be given proper attention and care besides the ribs. The alarming rate of heart defects and increasing death toll calls for the urgent need to improve the ECG platform with enhance machine learning technique the more accurate predictive diagnosis of heart conditions like arrhythmia, stroke and other cardio-vascular disorders. This paper proposed a machine learning approach using ANN and objective methodology to analyse and predict heartbeat condition using heart dataset of 303 patients from kaggle dataset repository and the accuracy level was 88.52 with a validation loss of 34.31 respectively which indicates that with further work with more advance machine learning algorithm the predictive capacity of the model could be optimized.
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Idayana ALABERE (2026) studied Normal and abnormal ECG heartbeat conditions (arrhythmia, stroke, and other cardiovascular disorders) (n=303). Artificial Neural Network (ANN) machine learning model was evaluated on Predictive accuracy for heartbeat condition. An Artificial Neural Network (ANN) model predicted heartbeat conditions in 303 patients with 88.52% accuracy and a validation loss of 34.31.
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