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在囊性纤维化中使用AI驱动的风险预测和分类,利用AttentiveLSTM和Fox Wolf Optimizer

Ashwini A Pandagale¹, Lalit V Patil¹

¹Department of Computer Engineering, 210030 Smt Kashibai Navale College of Engineering, SPPU , Pune, Maharashtra, 411041, India.

Statistical applications in genetics and molecular biology

|February 16, 2026

概括

这项研究引入了一个新的囊性纤维化风险预测框架 (CGRPF),使用先进的人工智能准确预测患者的风险. 该框架改进了目前用于更好的囊性纤维化 (CF) 管理的方法.

科学领域:

生物医学信息学生物医学信息学
计算生物学计算生物学
遗传学是一个遗传学.

背景情况:

囊性纤维化 (CF) 是一种由CFTR基因突变引起的遗传疾病.
目前对CF的调节器疗法受到基因变异覆盖范围和可访问性的限制.
准确的风险预测对于有效的CF管理至关重要.

研究的目的:

开发一种新的囊性纤维化风险预测框架 (CGRPF),以改善CF风险分层.
通过先进的计算方法解决当前CF管理策略的局限性.

主要方法:

使用的平均值归算用于处理缺失的数据.
使用狐狼优化器 (FWO) 进行功能选择.
实施了AttentiveLSTM模型来分析时间序列数据中的时间模式,以预测慢性疾病.
集成的完全连接和软max层,以加强对风险类别的分类.

主要成果:

在CFTR-2数据集上,CGRPF取得了高绩效.
关键指标包括97%的准确率,91%的精度,97%的回忆率和93%的F1分数.
该框架在囊性纤维化风险预测方面表现优于现有的最先进模型.

结论:

拟议的CGRPF在预测囊性纤维化风险方面表现卓越.
这一框架为改善CF患者管理和治疗策略提供了一个有前途的工具.
整合FWO和AttentiveLSTM为复杂疾病预测提供了一个强大的方法.

关键词:

在CFTR-2中使用.专注的LSTM注意力慢性疾病是一种慢性疾病.囊性纤维化症是什么狐狼优化器的优化器

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