RESUMO
Cardiovascular diseases(CVDs) are the world's leading cause of death. Endothelial Dysfunction is an early stage of cardiovascular diseases and can effectively be used to detect the presence of the CVDs, monitor its progress and investigate the effectiveness of the treatment given. This study proposes a reliable approach for the screening of endothelial dysfunction via machine learning, using features extracted from a combination of Plethysmography, Digital Thermal Monitoring, biological features (age and gender) and anthropometry (BMI and pulse pressure). This case control study includes 55 healthy subjects and 45 subjects with clinically verified CVDs. Following the feature engineering stage, the results were subjected to dimension reduction and 5-fold cross-validation where it was observed that models Logistic Regression and Linear Discriminant provided the highest accuracies of 84% and 81% respectively. We propose that this study can be used as an efficient guide for the non-invasive screening of endothelial dysfunction.
