Prediction of Heart Disease Arrhythmia Using Machine Learning

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.