AI-Driven Cardiac Lymphedema Prediction Using Clinical Data - kumar S, Nithish; K Selvavinayaki, K Selvavinayaki; Sneha B, Sneha B; R Anitha, R Anitha (Journal of Neonatal Surgery, 2025)
Cardiac lymphedema represents a condition where there is dysfunction in the lymphatic drainage system of the heart, leading to the accumulation of lymph fluid, which can contribute to a variety of cardiovascular complications, including heart failure, myocardial inflammation, and arrhythmias. These complications, when not managed effectively, pose significant risks to patient health. Early diagnosis of cardiac lymphedema remains a significant challenge due to its subtle clinical presen- tation, which often leads to misdiagnosis or delayed diagnosis, as well as the limitations of traditional diagnostic methods. This research presents an innovative approach to predicting cardiac lymphedema through the application of a gradient- boosting algorithm integrated into a web-based decision support system designed to aid clinicians in diagnosing the condition. The model utilizes a carefully curated dataset, including a range of clinical and physiological parameters, to achieve high accuracy in performing binary classification tasks. The web-based application provides a user-friendly interface that enables clinicians to input patient-specific data and receive predictive insights regarding the likelihood of cardiac lymphedema, which can significantly improve clinical decision-making. The integration of machine learning into the diagnostic process highlights its potential to bridge existing gaps in healthcare diagnostics and offers a promising tool for enhancing early detection and treatment outcomes in patients with cardiac lymphedema. Index Terms—Cardiac Lymphedema, Lymphatic Drainage System, Heart Failure, Myocardial Inflammation, Arrhythmias, Cardiovascular Complications, Early Diagnosis, Clinical Pre- sentation, Traditional Diagnostic Methods, Gradient-Boosting Algorithm, Web-Based Decision Support System, Binary Classifi- cation, Clinical and Physiological Parameters, Machine Learning in Healthcare, Predictive Analytics, Healthcare Diagnostics, Early Detection, Treatment Outcomes, User-Friendly Interface, Patient- Specific Data