北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (6): 39-0.

• 智慧医疗 • 上一篇    下一篇

改进鲸鱼优化LightGBM的可解释性心脏病风险预测模型

王洁,李金泽,王子曈,周淑怡,彭岩   

  1. 首都师范大学
  • 收稿日期:2023-01-17 修回日期:2023-03-31 出版日期:2023-12-28 发布日期:2023-12-29
  • 通讯作者: 彭岩 E-mail:pengyan@cnu.edu.cn
  • 基金资助:
    全国教育科学规划-教育部重点课题(DLA190426)

An Interpretable Prediction Model for Heart Disease Risk Based on Improved Whale Optimized LightGBM

  • Received:2023-01-17 Revised:2023-03-31 Online:2023-12-28 Published:2023-12-29

摘要: 针对现有心脏病风险预测模型准确率较低且可解释性较差等问题,提出了一种改进鲸鱼优化轻量级梯度提升机(LightGBM)的可解释性心脏病风险预测模型。首先,使用深度自编码器有效地实现了数据降维;然后,采用halton序列初始化种群、非线性收敛因子和动态螺旋更新等策略改进鲸鱼优化算法,以获取LightGBM超参数的全局最优解;最后,引入沙普利加性解释方法对该模型的重要特征进行可解释性分析。与其他主流降维方法及分类模型的对比实验表明,该模型具有更高的预测精度,能高效地提取心脏病风险的潜在特征。

关键词: 心脏病风险预测, 深度自编码器, 改进鲸鱼优化算法; 轻量级梯度提升机, 可解释性

Abstract: Aiming at the problems of low accuracy and poor interpretability of existing heart disease risk prediction models, an interpretable heart disease risk prediction model based on improved whale optimized light gradient boosting machine (LightGBM) is proposed. First, the deep auto-encoder is used to effectively reduce the data dimensionality. Then, the whale optimization algorithm is improved by various strategies to obtain the global optimal solution of LightGBM hyper-parameter, including halton sequence initialization population, nonlinear convergence factor and dynamic spiral update. Finally, the important features of the proposed model are explained and analyzed by the shapley additive explanations method. Compared with other mainstream dimensionality reduction methods and classification models, experimental results show that the proposed model can obtain higher prediction accuracy and can efficiently extract the potential characteristics of heart disease risk factors.

Key words: heart disease risk prediction, deep auto-encoder, improved whale optimization algorithm, light gradient boosting machine, interpretability

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