智能心血管疾病诊断使用深度学习增强神经网络与殖民地优化
Biao Xia1, Nisreen Innab2, Venkatachalam Kandasamy3
1Medical Equipment Department, Changzhou No2 Hospital Nanjing Medical University, Changzhou, 213164, Jiangsu, China. stephenxiabiao@sina.com.
Scientific reports
|September 18, 2024
概括
一个新的智能心血管疾病诊断模型 (ICVD-ACOEDL) 使用殖民地优化和深度学习来准确检测心血管疾病 (CVD). 这种方法通过优化复杂医疗数据中的特征选择和超参数来改善早期诊断.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 心血管疾病 (CVD) 患病率在全球范围内不断增加,需要早期和精确的诊断.
- 由于复杂的医疗保健数据集和难以识别特征连接以进行准确的预测,诊断心血管疾病具有挑战性.
- 深度学习 (DL) 和机器学习 (ML) 对分析大型医疗数据集具有前景,但在培训和验证准确性方面面临挑战.
研究的目的:
- 开发一种先进的智能心血管疾病诊断模型 (ICVD-ACOEDL).
- 通过特征选择和超参数优化,提高CVD分类的准确性和效率.
- 解决处理复杂医疗数据的现有方法的局限性,以可靠地检测心血管疾病.
主要方法:
- 数据预处理使用min-max缩放以保持一致性.
- 通过群优化 (ACO) 进行特征选择 (FS),以确定最佳特征子集.
- 使用贝叶斯优化对深度学习增强神经网络 (DLENN) 分类器进行超参数优化.
主要成果:
- 与基准医疗数据集的现有技术相比,ICVD-ACOEDL模型显示出更高的性能.
- 由ACO驱动的特征选择显著提高了DLENN分类器的性能.
- 贝叶斯优化有效调整了DLENN的超参数,提高了CVD分类的准确性.
结论:
- ICVD-ACOEDL模型为提高CVD分类效率和准确性提供了一个强大的解决方案.
- 对FS的ACO集成,min-max缩放和贝叶斯优化为现实世界医疗应用提供了可行的方法.
- 这种智能诊断系统有可能对心血管疾病诊断和患者的治疗结果产生重大影响.
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