An optimized machine learning model based on hematological indicators for the noninvasive identification of baicalin’s therapeutic effects in pulmonary hypertension

Pulmonary hypertension (PH) is a progressive cardiopulmonary disorder with high mortality, necessitating non-invasive methods for early detection and treatment evaluation. In this paper, this study proposes a novel machine learning model for non-invasively identifying the therapeutic effects of Baicalin in PH using routine hematological indicators. The core innovation is an enhanced Bat Algorithm (BA) variant, termed RGBA, which integrates an Elite-based Random Walk Strategy (ERWS) and an Elite Guided Strategy (EGS) to achieve a superior balance between exploration and exploitation. And the RGBA demonstrated significantly improved global search capability in IEEE CEC 2014 benchmark tests, outperforming several state-of-the-art meta-heuristic algorithms. Subsequently, a binary version of RGBA (bRGBA) was developed and combined with a Kernel Extreme Learning Machine (KELM) classifier within a wrapper-based feature selection framework, forming the bRGBA-KELM model. Applied to a dedicated PH dataset from murine models, bRGBA-KELM achieved a prediction accuracy of 97.43% via ten-fold cross-validation, outperforming nine comparable hybrid models. Critically, it identified four key blood biomarkers—Red Blood Cell count (RBC), Hemoglobin (HGB), Mean Corpuscular Volume (MCV), and Hematocrit (HCT)—that are mechanistically linked to PH pathogenesis and modulated by Baicalin treatment. In conclusion, the proposed RGBA offers a robust optimization tool, while the bRGBA-KELM model provides a clinically viable, non-invasive technical reference for early PH prediction and therapeutic assessment.