Background: While machine learning (ML) is widely applied in cardiology, a critical research gap persists. The incremental diagnostic value of routine blood tests for classifying cardiovascular disease (CVD) remains largely unquantified, and many models operate as non-interpretable “black boxes,” limiting their clinical adoption. This study aims to address these gaps by quantifying the contribution of readily available laboratory panels and demonstrating the utility of transparent diagnostic modeling within a real-world clinical cohort. Methods: We conducted a retrospective study on the clinical data of 896 adult patients from a hospital database. A baseline feature set (demographics, vital signs) was compared against an enhanced set that additionally included results from routine hematology and biochemistry panels. Five machine learning classifiers were trained and evaluated. To ensure transparency, SHAP (SHapley Additive exPlanations) analysis, a key component of explainable AI (XAI), was used to interpret the predictions of the top-performing model. Results: The inclusion of routine blood tests consistently and significantly improved the performance of all classifiers. The XGBoost model demonstrated the best performance (accuracy 91.62%, precision 95.00%, recall 87.36%). Critically, SHAP analysis identified aspartate aminotransferase (AST), glucose, and creatinine as the most significant biomarkers, providing clear, interpretable insights into the biochemical drivers of the model’s predictions. Conclusion: Routine laboratory markers contain a strong, interpretable signal indicative of CVD that is crucial for accurate risk stratification. These findings underscore the diagnostic relevance of common blood biomarkers and demonstrate how explainable AI can transform routine clinical data into transparent and actionable cardiovascular insights. Further validation in larger and demographically diverse cohorts is warranted.

Background: Osteoarthritis (OA) is a prevalent chronic degenerative disorder, with coxarthrosis (hip OA) and gonarthrosis (knee OA) representing its most significant clinical manifestations. While diagnosis typically relies on imaging, such methods can be resource-intensive and insensitive to early disease trajectories. Objective: This study aims to achieve the differential diagnosis of coxarthrosis and gonarthrosis using solely routine preoperative clinical and laboratory data, benchmarking state-of-the-art machine learning algorithms. Methods: A retrospective analysis was conducted on 893 patients (617 with knee OA, 276 with hip OA) from a clinical hospital in Almaty, Kazakhstan. The study evaluated a diverse portfolio of models, including gradient boosting decision trees (LightGBM, XGBoost, CatBoost), deep learning architectures (RealMLP, TabDPT, TabM), and the pretrained tabular foundation model RealTabPFN v2.5. Results: The RealTabPFN v2.5 (Tuned) model achieved superior performance, recording a mean ROC–AUC of 0.9831, accuracy of 0.9485, and an F1-score of 0.9474. SHAP interpretability analysis identified heart rate (66.2%) and age (18.1%) as the dominant predictors driving the model’s decision-making process. Conclusion: Pretrained tabular foundation models demonstrate exceptional capability in distinguishing OA subtypes using limited clinical datasets, outperforming traditional ensemble methods. This approach offers a practical, high-performance triage tool for primary clinical assessment in resource-constrained settings.

Osteoarthritis (OA) of the hip (coxarthrosis) and knee (gonarthrosis) is a leading cause of disability worldwide. Differential diagnosis typically relies on imaging modalities such as X-rays and Magnetic Resonance Imaging (MRI). However, advanced imaging can be expensive and inaccessible, highlighting the need for non-invasive diagnostic tools. This study aimed to develop and validate an interpretable machine learning model to distinguish between hip and knee osteoarthritis using standard preoperative clinical and laboratory data. This model is designed to assist physicians in prioritizing whether to order a hip or a knee X-ray first, thereby saving time and medical resources. The study utilized retrospective data from 1792 patients treated at the City Clinical Hospital in Almaty, Kazakhstan. After applying inclusion and exclusion criteria, five machine learning algorithms were used for training and evaluation: Decision Tree, Random Forest, Logistic Regression, XGBoost, and CatBoost. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were employed to interpret predictions and determine the contribution of each feature. The XGBoost model demonstrated the best performance, achieving an accuracy of 93.85%, a precision of 95.15%, a recall of 90.51%, and an F1-score of 92.41%. SHAP analysis revealed that age, glucose and leukocyte levels, urea, and BMI made the greatest contributions to the model’s predictions, while local analysis using LIME indicated that age, leukocyte levels, glucose, erythrocytes, and platelets were the most influential features. These findings support the use of machine learning for cost-effective early osteoarthritis triage using routine preoperative data.