Prediction of Heart Disease Arrhythmia Using Machine Learning
Year: 2025
Type: article
Abstract: Arrhythmia is a condition where the heartbeat becomes irregular—either too fast, too slow, or unpredictable. If not detected in time, it can lead to serious health issues like stroke, heart failure, or sudden cardiacarrest. Traditional methods of diagnosis often depend on expensive equipment and expert doctors, which sometimes causes delays in treatment. This study focuses on usingmachine learning to detect arrhythmia early and more accurately. We've built a system using models like Random Forest and LSTM (Long Short-Term Memory) to analyze ECG data and identify irregular heartbeats. Our results show that these models can find signs0 of arrhythmia more effectively than traditional methods. Research in this area generally focuses on three types of data— standard medical records, ECG and PCG signals, and X-ray images.
Author: Shreya Dhananjay Sawade
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6949499738
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