Authors - Sanjana Priyadarsini, Choudhary Aman Kumar Roy, Ashlesha Shree Bajpai, Rajdeep Banerjee, Shivali Sharma, Ranjita Kumari Dash Abstract - Today, machine learning methods are quickly being adopted in healthcare. In numerous instances, it has been observed that datadriven approaches have increased reliance of medical data analysis and disease detection by about 60-70%. It is important to diagnose cardiac arrhythmias early using electrocardiogram (ECG) analysis, as timely diagnosis can prevent severe complications and loss of life. Most ECG datasets are however not balanced, with normal beats by far outnumbering abnormal ones and causing the models to underperform on rare but significant cases. In this work, Logistic Regression is used as a baseline model. To correct this imbalance, Class weighting and Synthetic Minority over-sampling Technique (SMOTE) are applied. These techniques help the model detect rare heartbeat patterns more reliably and miss fewer abnormalities. This paper shows that addressing class imbalance can make ECG-based classification systems more accurate and clinically valid.
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