Artificial Intelligence Assisted Prediction in Acute Rheumatic Fever Patients with Carditis: The Utility of Complete Blood Count and Inflammatory Markers

Objective: Acute rheumatic fever (ARF) is a serious inflammatory disease that results from an autoimmune reaction triggered by a group A beta-hemolytic streptococcal infection, primarily affecting cardiac tissues.It remains the primary etiology of acquired cardiac disorders in children worldwide, especially in developing countries.The present study seeks to examine the potential of inflammatory indices derived from the complete blood count and of machinelearning algorithms for the early diagnosis of the development of carditis associated with ARF. Methods:In this retrospective study, 68 patients diagnosed with ARF and 71 healthy control subjects were examined between November 2020 and December 2025.Demographic data, blood tests, and transthoracic echocardiography reports were evaluated.Various inflammatory biomarkers, such as the neutrophil-monocyte index, neutrophil-platelet index, neutrophil-lymphocyte ratio, and systemic immune-inflammation index, were calculated.JASP 0.95.2 software and the random forest algorithm were used for data analysis and machine-learning modeling.Results: As a result of the analyses, notable disparities achieved statistical were found between the control and patient groups in neutrophil (p=0.037),neutrophil-platelet index (p=0.025),neutrophil-lymphocyte ratio (p=0.012), and systemic immune-inflammation index (p=0.016)parameters.The machinelearning random forest model achieved a high test accuracy of 81.5% when using indices with significant p-values.In the feature-importance analysis, parameters such as systemic immune-inflammation index, monocyte, eosinophil and neutrophil were key determinants of the model's classification performance.Conclusion: This study has demonstrated the potential of inflammatory indices derived from the complete blood count, combined with machine-learning algorithms, to predict, early and with high accuracy, the development of carditis associated with ARF.The significant correlations of markers such as neutrophil-lymphocyte ratio, neutrophil-platelet index, and SII with the presence of carditis, along with the predictive value of machine-learning models, suggest that complete blood count parameters may provide a critical advantage in the diagnosis and early detection of subclinical carditis.