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.