According to the World Health Organization (WHO), cardiovascular illnesses, including arrhythmia, are the primary cause of mortality globally, responsible for over 31% of all fatalities each year. To reduce mortality, early and precise diagnosis is essential. Although the analysis of electrocardiograms (ECGs) is the primary means of detecting arrhythmias, it depends significantly on the expertise and subjectivity of the health professional reading and interpreting the ECG, and errors may occur in detection. Artificial intelligence provides tools, techniques, and models that can support health professionals in detecting arrhythmias. However, these tools are based only on ECG data, of which the process of obtaining is an invasive, high-cost method requiring specialized equipment and personnel. Smartwatches feature sensors that can record real-time signals indicating the heart’s behavior, such as ECG signals and heart rate. Using this approach, we propose a machine learning- and deep learning-based approach for detecting arrhythmias using heart rate data obtained with smartwatches. Heart rate data were collected from 252 patients with and without arrhythmias who attended a clinic in Arequipa, Peru. Heart rates were also collected from 25 patients who wore smartwatches. Ten machine learning algorithms were implemented to generate the most effective arrhythmia recognition model, with the decision tree algorithm being the most suitable. The results were analyzed using accuracy, sensitivity, and specificity metrics. Using Holter data yielded values of 93.2%, 91.89%, and 94.59%, respectively. Using smartwatch data yielded values of 70.83%, 91.67%, and 50%, respectively. These results indicate that our model can effectively recognize arrhythmias from heart rate data. The high sensitivity score suggests that our model adequately recognizes true positives; that is, patients with arrhythmia. Likewise, its specificity suggests an adequate recognition of false positives.

Heart rate abnormalities, including tachycardia and bradycardia, are among the leading global causes of morbidity, necessitating continuous and accurate monitoring for early detection and intervention. This study introduces an innovative smartwatch-based cardiac monitoring system that integrates fractal geometry analysis, dynamical systems modeling, and clustering techniques to enhance diagnostic precision. Unlike conventional smartwatch-based monitoring systems, this approach employs advanced mathematical modeling to identify nonlinear patterns in heart rate dynamics, enabling more precise differentiation between normal and pathological conditions. The system was developed using the CRISP-DM methodology, ensuring a structured and data-driven implementation. A mobile application, “My Cardio,” was designed for Android-based smartwatches, enabling the collection of real-time heart rate data and cloud storage for subsequent fractal-based processing. Additionally, a clustering analysis was performed on data from patients with and without cardiac history, identifying three distinct patient groups based on heart rate characteristics. Cluster 0 included individuals with lower, stable heart rates; Cluster 1 represented intermediate variations; and Cluster 2 comprised patients with significantly elevated heart rates associated with higher clinical risk. The findings were statistically validated and visualized, demonstrating that integrating clustering techniques with fractal geometry enhances the detection of clinically relevant cardiac patterns. Furthermore, a usability assessment using the System Usability Scale (SUS) yielded a score of 80.3 or higher, confirming high user acceptance and feasibility for widespread adoption. This study differentiates itself from existing approaches by combining wearable technology with advanced computational techniques to enhance cardiac diagnosis and monitoring. The results underscore the potential of smartwatch-based systems as a noninvasive, intelligent alternative for continuous cardiovascular assessment, paving the way for future applications in digital cardiology and telemedicine.