Kucukakcali Zeynep, Cicek Ipek Balikci, Akbulut Sami
Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Turkey.
Department of Surgery, Inonu University Faculty of Medicine, Malatya 44280, Turkey.
Diagnostics (Basel). 2025 Sep 1;15(17):2219. doi: 10.3390/diagnostics15172219.
: This study aims to build an interpretable and accurate predictive model for myocardial infarction (MI) using Explainable Boosting Machines (EBM), a state-of-the-art Explainable Artificial Intelligence (XAI) technique. The objective is to identify and rank clinically relevant biomarkers that contribute to MI diagnosis while maintaining transparency to support clinical decision making. : The dataset comprises 1319 patient records collected in 2018 from a cardiology center in the Erbil region of Iraq. Each record includes eight routinely measured clinical and biochemical features, such as troponin, CK-MB, and glucose levels, and a binary outcome variable indicating the presence or absence of MI. After preprocessing (e.g., one-hot encoding, normalization), the EBM model was trained using 80% of the data and tested on the remaining 20%. Model performance was evaluated using standard metrics including AUC, accuracy, sensitivity, specificity, F1 score, and Matthews correlation coefficient. Feature importance was assessed to identify key predictors. Partial dependence analyses provided insights into how each variable affected model predictions. : The EBM model demonstrated excellent diagnostic performance, achieving an AUC of 0.980, an accuracy of 96.6%, sensitivity of 96.8%, and specificity of 96.2%. Troponin and CK-MB were identified as the top predictors, confirming their established clinical relevance in MI diagnosis. In contrast, demographic and hemodynamic variables such as age and blood pressure contributed minimally. Partial dependence plots revealed non-linear effects of key biomarkers. Local explanation plots demonstrated the model's ability to make confident, interpretable predictions for both positive and negative cases. : The findings highlight the potential of EBM as a clinically useful and ethical AI approach for MI diagnosis. By combining high predictive accuracy with transparency, EBM supports biomarker prioritization and clinical risk stratification, thus aligning with precision medicine and responsible AI principles. Future research should validate the model on multi-center datasets and explore additional features for broader clinical use.
本研究旨在使用可解释增强机器（EBM）构建一个用于心肌梗死（MI）的可解释且准确的预测模型，EBM是一种先进的可解释人工智能（XAI）技术。目标是识别并对有助于MI诊断的临床相关生物标志物进行排序，同时保持透明度以支持临床决策。数据集包含2018年从伊拉克埃尔比勒地区的一个心脏病中心收集的1319份患者记录。每份记录包括八个常规测量的临床和生化特征，如肌钙蛋白、肌酸激酶同工酶（CK-MB）和血糖水平，以及一个二元结果变量，表明是否存在MI。经过预处理（例如独热编码、归一化）后，使用80%的数据训练EBM模型，并在其余20%的数据上进行测试。使用包括曲线下面积（AUC）、准确率、灵敏度、特异性、F1分数和马修斯相关系数在内的标准指标评估模型性能。评估特征重要性以识别关键预测因子。部分依赖分析提供了关于每个变量如何影响模型预测的见解。EBM模型表现出优异的诊断性能，AUC为0.980，准确率为96.6%，灵敏度为96.8%，特异性为96.2%。肌钙蛋白和CK-MB被确定为首要预测因子，证实了它们在MI诊断中已确立的临床相关性。相比之下，年龄和血压等人口统计学和血流动力学变量的贡献最小。部分依赖图揭示了关键生物标志物的非线性效应。局部解释图展示了模型对阳性和阴性病例做出可靠、可解释预测的能力。研究结果突出了EBM作为一种对MI诊断具有临床实用性和符合伦理的人工智能方法的潜力。通过将高预测准确率与透明度相结合，EBM支持生物标志物的优先级排序和临床风险分层，从而符合精准医学和负责任的人工智能原则。未来的研究应在多中心数据集上验证该模型，并探索更多特征以用于更广泛的临床应用。
