一个基因算法辅助的基于超参数优化的组合模型用于呼吸系统疾病预测,使用可解释的AI
Balraj Preet Kaur1, Harpreet Singh2, Rahul Hans1
1Department of Computer Science and Engineering, DAV University, Jalandhar, Punjab, India.
PloS one
|December 2, 2024
概括
这项研究引入了一种先进的机器学习模型,用于预测COVID-19等呼吸系统疾病. 该模型使用遗传算法来进行超参数优化和灰狼优化来选择特征,从而实现更高的诊断准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 机器学习对于疾病诊断至关重要,特别是在COVID-19等严重的呼吸道疾病中.
- 早期诊断COVID-19对于减轻其严重的健康影响至关重要.
- 有效的机器学习部署需要强大的超参数优化和功能选择.
研究的目的:
- 开发一种改进的机器学习模型,用于预测呼吸系统疾病.
- 结合超参数优化和特征选择的先进技术.
- 通过组合方法和可解释的人工智能来提高预测效率.
主要方法:
- 使用遗传算法进行超参数优化.
- 通过二进制灰狼优化算法进行特征选择.
- 集成模型开发与堆叠分类器.
- 使用Shapely适应性解释 (SHAP) 值进行可解释的AI集成.
- 在墨西哥临床COVID-19数据集上的实验.
主要成果:
- 与现有方法相比,拟议的模型显示出更高的预测准确性.
- 在其他算法中,Adaboost算法在超参数优化后表现出色.
- 使用SHAP值来解释特征的重要性,提高模型的透明度.
结论:
- 开发的模型为准确和可解释的呼吸道疾病预测提供了一个有希望的方法.
- 优化的机器学习模型,特别是adapboost,可以显著帮助早期发现疾病.
- 整体方法和可解释AI的整合提高了诊断系统的可靠性和临床实用性.
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