Kucukakcali Zeynep, Balikci Cicek Ipek
Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey.
Medicina (Kaunas). 2025 Aug 29;61(9):1552. doi: 10.3390/medicina61091552.
: This study aims to evaluate the diagnostic potential of routinely available hematological parameters for acute myocardial infarction (AMI) by employing an Explainable Neural Network (ENN) model that combines high predictive accuracy with interpretability. : A publicly available dataset comprising 981 individuals (477 AMI patients and 504 controls) was analyzed. A broad set of hematological features-including white blood cell subtypes, red cell indices, and platelet-based markers-was used to train an ENN model. Bootstrap resampling was applied to enhance model generalizability. The model's performance was assessed using standard classification metrics such as accuracy, sensitivity, specificity, F1-score, and Matthews Correlation Coefficient (MCC). SHapley Additive exPlanations (SHAP) were employed to provide both global and individualized insights into feature contributions. : The study analyzed hematological and biochemical parameters of 981 individuals. The explainable neural network (ENN) model demonstrated excellent diagnostic performance, achieving an accuracy of 94.1%, balanced accuracy of 94.2%, F1-score of 93.9%, and MCC of 0.883. The AUC was 0.96, confirming strong discriminative ability. SHAP-based explainability analyses highlighted neutrophils (NEU), white blood cells (WBC), RDW-CV, basophils (BA), and lymphocytes (LY) as the most influential predictors. Individual- and class-level SHAP evaluations revealed that inflammatory and erythrocyte-related parameters played decisive roles in AMI classification, while distributional analyses showed narrower parameter ranges in healthy individuals and greater heterogeneity among patients. : The findings suggest that cost-effective, non-invasive blood parameters can be effectively utilized within interpretable AI frameworks to enhance AMI diagnosis. The integration of ENN with SHAP provides a dual benefit of diagnostic power and transparent rationale, facilitating clinician trust and real-world applicability. This scalable, explainable model offers a clinically viable decision-support tool aligned with the principles of precision medicine and ethical AI.
本研究旨在通过使用一种将高预测准确性与可解释性相结合的可解释神经网络（ENN）模型，评估常规可用血液学参数对急性心肌梗死（AMI）的诊断潜力。分析了一个包含981名个体（477名AMI患者和504名对照）的公开可用数据集。使用了广泛的血液学特征，包括白细胞亚型、红细胞指数和基于血小板的标志物，来训练一个ENN模型。应用自助重采样来提高模型的泛化能力。使用标准分类指标，如准确率、敏感性、特异性、F1分数和马修斯相关系数（MCC）来评估模型的性能。采用SHapley加法解释（SHAP）来提供对特征贡献的全局和个性化见解。该研究分析了981名个体的血液学和生化参数。可解释神经网络（ENN）模型表现出优异的诊断性能，准确率达到94.1%，平衡准确率为94.2%，F1分数为93.9%，MCC为0.883。曲线下面积（AUC）为0.96，证实了较强的判别能力。基于SHAP的可解释性分析突出了中性粒细胞（NEU）、白细胞（WBC）、红细胞分布宽度变异系数（RDW-CV）、嗜碱性粒细胞（BA）和淋巴细胞（LY）是最有影响力的预测因子。个体和类别水平的SHAP评估表明，炎症和红细胞相关参数在AMI分类中起决定性作用，而分布分析显示健康个体的参数范围较窄，患者之间的异质性更大。研究结果表明，具有成本效益的非侵入性血液参数可以在可解释的人工智能框架内有效利用，以加强AMI诊断。ENN与SHAP的整合提供了诊断能力和透明原理的双重好处，促进了临床医生的信任和实际应用。这种可扩展、可解释的模型提供了一种符合精准医学和伦理人工智能原则的临床可行决策支持工具。
