Hidayat Erwin Yudi, Astuti Yani Parti, Dewi Ika Novita, Salam Abu, Soeleman Moch Arief, Hasibuan Zainal Arifin, Yousif Ahmed Sabeeh
Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia.
Research Center for Intelligent Distributed Surveillance and Security, Universitas Dian Nuswantoro, Semarang, Indonesia.
Healthc Inform Res. 2024 Jul;30(3):234-243. doi: 10.4258/hir.2024.30.3.234. Epub 2024 Jul 31.
This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection.
Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies.
The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks.
The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.
本研究旨在使用基于遗传算法（GA）的卷积神经网络（CNN）特征工程方法优化早期冠心病（CHD）预测。我们试图通过利用遗传算法在冠心病检测中实现卓越的预测性能，来克服传统超参数优化技术的局限性。
利用遗传算法进行超参数优化，我们在复杂的组合空间中导航，以确定CNN模型的最佳配置。我们还采用信息增益进行特征选择优化，将冠心病数据集转换为类似图像的输入，用于CNN架构。该方法的有效性与传统优化策略进行了对比。
先进的基于遗传算法的CNN模型优于传统方法，准确率大幅提高。优化后的模型提供了一个有前景的准确率范围，在超参数优化中峰值为85%，在与朴素贝叶斯、支持向量机、决策树、逻辑回归和随机森林等机器学习算法集成时，对于二元和多类冠心病预测任务的准确率均达到100%。
将遗传算法集成到CNN特征工程中是提高冠心病预测准确率的有力技术。这种方法具有高度的预测可靠性，可为人工智能驱动的医疗保健领域做出重大贡献，并有可能用于早期冠心病检测的临床部署。未来的工作将集中于扩展该方法，以涵盖更广泛的冠心病数据，并可能与可穿戴技术集成以进行连续健康监测。
