Addisu Eshetie Gizachew, Yirga Tahayu Gizachew, Yirga Hailu Gizachew, Yehuala Alemu Demeke
Department of Information Systems, College Informatics, University of Gondar, Gondar, Ethiopia.
Department of Computer Science, College of Natural and Computational Science, Mekdela Amba University, Tulu Awuliya, Ethiopia.
Front Artif Intell. 2025 Feb 25;8:1504281. doi: 10.3389/frai.2025.1504281. eCollection 2025.
Heart disease is a leading cause of mortality worldwide, making accurate early detection essential for effective treatment and management. This study introduces a novel hybrid machine-learning approach that combines transfer learning using the VGG16 convolutional neural network (CNN) with various machine-learning classifiers for heart disease detection. A conditional tabular generative adversarial network (CTGAN) was employed to generate synthetic data samples from actual datasets; these were evaluated using statistical metrics, correlation analysis, and domain expert assessments to ensure the quality of the synthetic datasets. The dataset comprises tabular data with 13 features, which are reshaped into an image-like format and resized to 224x224x3 to meet the input requirements of the VGG16 model. Feature extraction is performed using VGG16, and the extracted features are then fused with the original tabular data. This combined feature set is then used to train various machine learning models, including Support Vector Machines (SVM), Gradient Boosting, Random Forest, Logistic Regression, K-nearest neighbors (KNN), and Decision Trees. Among these models, the VGG16-Random Forest hybrid achieved notable results across all evaluation metrics, including 92% accuracy, 91.3% precision, 92.2% recall, 91.82% specificity, 92.2% sensitivity, and 91.75% F1-score. The hybrid models were also evaluated using unseen datasets to assess the generalizability of the proposed approaches, with the VGG16-Random Forest combination showing relatively promising results. Additionally, explainability is integrated into the model using SHAP values, providing insights into the contribution of each feature to the model's predictions. This hybrid VGG16-ML approach demonstrates the potential for highly accurate and interpretable heart disease detection, offering valuable support in clinical decision-making processes.
心脏病是全球主要的死亡原因之一，因此准确的早期检测对于有效治疗和管理至关重要。本研究介绍了一种新型的混合机器学习方法，该方法将使用VGG16卷积神经网络（CNN）的迁移学习与各种机器学习分类器相结合，用于心脏病检测。采用条件表格生成对抗网络（CTGAN）从实际数据集中生成合成数据样本；使用统计指标、相关性分析和领域专家评估对这些样本进行评估，以确保合成数据集的质量。该数据集包含具有13个特征的表格数据，这些数据被重塑为类似图像的格式，并调整大小为224x224x3，以满足VGG16模型的输入要求。使用VGG16进行特征提取，然后将提取的特征与原始表格数据融合。然后，使用这个组合特征集来训练各种机器学习模型，包括支持向量机（SVM）、梯度提升、随机森林、逻辑回归、K近邻（KNN）和决策树。在这些模型中，VGG16-随机森林混合模型在所有评估指标上都取得了显著成果，包括92%的准确率、91.3%的精确率、92.2%的召回率、91.82%的特异性、92.2%的灵敏度和91.75%的F1分数。还使用未见过的数据集对混合模型进行评估，以评估所提出方法的泛化能力，VGG16-随机森林组合显示出相对有前景的结果。此外，使用SHAP值将可解释性集成到模型中，从而深入了解每个特征对模型预测的贡献。这种VGG16-机器学习混合方法展示了高精度和可解释的心脏病检测的潜力，为临床决策过程提供了有价值的支持。
