Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acute PE population could significantly improve diagnosis and treatment, potentially reducing mortality rates. This study evaluates the performance of LogNNet and supervised machine learning (ML) models for diagnosing RVD using a repeated stratified hold-out validation procedure. An ensemble-based LogNNet model is proposed for practical application. The LogNNet model identified gender, coronary artery disease, Comorbid Disease (especially hypertension), age (above 74-years), Thrombus segment and un/bilateral Thrombus as the most significant predictors for RVD diagnosis. Additionally, combinations of these features demonstrated high predictive power. LogNNet achieved robust results with only a few selected features, making it suitable for applications in resource-limited environments. LogNNet provides a practical and accessible tool for early RVD detection using PE patient data and has been shown to support applications in healthcare innovations aimed at improving patient outcomes and resilience in edge devices, clinical decision support systems, and challenging environments. Furthermore, these findings could be used as promising applications by integrating with advances in digital health and human health monitoring systems, such as bionic clothing and smart sensor networks.

Diagnostic models; Edge computing; Feature selection; LogNNet; Machine learning; Medical IoT; Predictive analytics; Pulmonary embolism; Right ventricular dysfunction; Risk assessment; Thrombosis; Edge computing; Feature selection; LogNNet; Machine learning; Medical IoT; Predictive analytics; Pulmonary embolism; Right ventricular dysfunction; Risk assessment; Thrombosis