Erwin Yudi Hidayat, Yani Parti Astuti, Ika Novita Dewi, Abu Salam, Moch Arief Soeleman, Zainal Arifin Hasibuan, Ahmed Sabeeh Yousif
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The efficacy of this method was benchmarked against traditional optimization strategies.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"234-243"},"PeriodicalIF":2.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333810/pdf/","citationCount":"0","resultStr":"{\"title\":\"Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance.\",\"authors\":\"Erwin Yudi Hidayat, Yani Parti Astuti, Ika Novita Dewi, Abu Salam, Moch Arief Soeleman, Zainal Arifin Hasibuan, Ahmed Sabeeh Yousif\",\"doi\":\"10.4258/hir.2024.30.3.234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. 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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.</p>\",\"PeriodicalId\":12947,\"journal\":{\"name\":\"Healthcare Informatics Research\",\"volume\":\"30 3\",\"pages\":\"234-243\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333810/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Healthcare Informatics Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4258/hir.2024.30.3.234\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/31 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare Informatics Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4258/hir.2024.30.3.234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/31 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0
摘要
研究目的本研究旨在利用基于遗传算法(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 GA 来克服传统超参数优化技术的局限性,从而在 CHD 检测中获得卓越的预测性能:方法:利用 GA 进行超参数优化,我们在复杂的组合空间中进行导航,以确定 CNN 模型的最佳配置。我们还利用信息增益进行特征选择优化,将慢性阻塞性肺病数据集转化为类似图像的 CNN 架构输入。结果显示,基于 GA 的先进 CNN 模型优于传统的优化策略:结果:基于 GA 的先进 CNN 模型优于传统方法,准确率大幅提高。优化后的模型在二元和多分类 CHD 预测任务中的准确率范围很广,在超参数优化中达到了 85% 的峰值,与机器学习算法(即奈夫贝叶斯、支持向量机、决策树、逻辑回归和随机森林)集成后的准确率为 100%:结论:将 GA 集成到 CNN 特征工程中是提高 CHD 预测准确性的有力技术。这种方法具有很高的预测可靠性,能为人工智能驱动的医疗保健领域做出重大贡献,并有可能应用于早期冠心病的临床检测。未来的工作将侧重于扩展该方法,以涵盖更广泛的冠心病数据集,并有可能与可穿戴技术相结合,用于持续健康监测。
Objectives: 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.
Methods: 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.
Results: 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.
Conclusions: 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.