Implementation of a Machine Learning Algorithm for the Detection of Cardiovascular Diseases in Adult Patients in Public Hospitals of Lima, Peru, 2023

Introduction: Cardiovascular diseases are one of the leading causes of death worldwide. In Lima, Peru, public hospitals face significant challenges in the early and accurate diagnosis of these diseases due to limited resources and trained personnel. The implementation of machine learning (ML) algorithms offers a promising solution to improve the detection and management of cardiovascular diseases. Objective: The objective of this study is to implement and evaluate a machine learning algorithm for the detection of cardiovascular diseases in adult patients attended to in public hospitals of Lima, Peru, in the year 2023. Methodology: Medical data from 10,000 adult patients were collected, including medical histories, laboratory test results, and electrocardiogram (ECG) records from various public hospitals in Lima. The data were cleaned and normalized to ensure their quality and consistency. A classification algorithm based on deep neural networks was selected. The model was trained using 80% of the data and validated with the remaining 20%. Metrics of accuracy, sensitivity, and specificity were used to evaluate the model's performance. Results: The model achieved an accuracy of 92% in detecting cardiovascular diseases. The sensitivity was 89%, indicating that the model correctly identified 89% of positive cases. The specificity reached 94%, meaning the model correctly identified 94% of negative cases. Conclusion: The implementation of the machine learning algorithm proved effective for the detection of cardiovascular diseases in adult patients in public hospitals in Lima, Peru. With high accuracy, sensitivity, and specificity, this approach has the potential to significantly improve medical care and patient outcomes in resource-limited settings. Integrating this system into clinical processes is recommended to maximize its positive impact on public health.