Modeling of a System for Managing and Analyzing Heart Failure Data
Cardiovascular diseases encompass multiple influencing factors and contribute to approximately 15 million deaths worldwide. The medical community has already identified the triggers of these ailments, which play a pivotal role in monitoring conditions such as Heart Failure. These triggers include unmodifiable risk factors like gender, age, and genetic predisposition, as well as preventable factors such as smoking, hypertension, diabetes, and alcohol consumption. Diverse studies indicate that patients who develop Heart Failure often encounter difficulties in maintaining an adequate self-care routine. The utilization of technology is a familiar practice for both healthcare professionals and patients in managing various conditions. When applied to Heart Failure, this approach offers valuable opportunities to address the challenges associated with disease management. Hence, regular patient monitoring assumes a vital role in maintaining disease stability and ensuring patient quality of life. In this context, the present work brings forth a comprehensive literature review, the purpose of which was to define the essential criteria for constructing an effective data model. This roster of criteria includes prognostic variables that play a fundamental role in managing patients affected by Heart Failure. With these requirements delineated, the relational model described in this study was employed to carry out the implementation of a database capable of managing information originating from a system designed for monitoring individuals with Heart Failure.