• Priti Suryawanshi
• Dr. Shailesh Hambarde

Arrhythmia disease is a common disorder that affects the heart's rhythm and can lead to serious complications. The accurate prediction of arrhythmia is crucial for early diagnosis and effective treatment of patients. In recent years, machine learning algorithms have emerged as a promising approach for predicting arrhythmia disease. Early and precise detection of cardiac arrhythmias is crucial for improved patient outcomes. This investigation delves into the efficacy of a Random Forest (RF) classifier for automated arrhythmia detection using electrocardiogram (ECG) data. The research evaluates the RF model's performance on a publicly available ECG dataset, benchmarking it against existing methodologies. The findings substantiate the effectiveness of the RF classifier in arrhythmia detection, achieving superior accuracy, robustness, and interpretability.

Arrhythmia disease, Heart's rhythm ,Complications, Prediction ,Machine learning algorithms, Random Forest

"Prediction of Cardiac Arrhythmia using Random Forest Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.d199-d204, March-2024, Available :http://www.jetir.org/papers/JETIR2403323.pdf

"Prediction of Cardiac Arrhythmia using Random Forest Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppd199-d204, March-2024, Available at : http://www.jetir.org/papers/JETIR2403323.pdf