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During the pandemic, patients with underlying chronic diseases such as cardiovascular disease died at a higher rate than the general population. It is widely accepted that monitoring a patient's physiological state is an effective method of detecting changes in a patient's baseline condition. Machine learning algorithms were used in this study to analyze ECG data from a wearable device that was developed in-house. The wearable device was created with the help of disposable electrodes attached directly to the chest; an ECG sensor can be used to detect every heartbeat. The electrodes on the ECG sensor will convert a heartbeat into an electrical signal. It is possible to measure continuous heartbeats with high accuracy and provide rate data for heartbeats using extremely light and thin sensors. To assess and evaluate the device's performance, the target data was used in conjunction with a web dataset used in the training and validation of the model, which was developed using a machine learning algorithm. Based on the datasets used, it is possible to categorize ECGs effectively.