使用可解释机器学习和数学模型检测阿尔茨海默病
Krishna Mahapatra1, R Selvakumar1
1Department of Mathematics, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Journal of medical physics
|April 21, 2025
概括
这项研究使用数学建模和机器学习从MRI扫描中对阿尔茨海默病 (AD) 阶段进行分类,使用高斯素朴贝叶斯分类器达到95.45%的准确性.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 数学建模的数学建模
背景情况:
- 阿尔茨海默病 (AD) 诊断依赖于准确的分期.
- 磁共振成像 (MRI) 提供了详细的大脑结构信息.
- 目前的分类方法可以提高准确性和效率.
研究的目的:
- 开发一种新的方法来使用MRI扫描对阿尔茨海默病 (AD) 的四个阶段进行分类.
- 将数学建模与机器学习 (ML) 整合起来,以提高诊断能力.
- 在神经成像分析中探索物理概念的实用性,例如惯性时刻张量.
主要方法:
- 用惯性矩 (MI) 张量原理将MRI像素值矩阵映射到2x2矩阵中.
- 惯性张量的自身值与ML算法一起使用.
- 评估了各种ML模型的性能,以对AD阶段进行分类.
主要成果:
- 高斯的天真贝叶斯分类器以95.45%的准确度显示出最高的准确度.
- 综合数学和机器学习方法在区分AD阶段方面被证明是有效的.
- 在多个ML模型中进行了性能比较.
结论:
- 拟议的方法在MRI的阿尔茨海默病 (AD) 阶段确定方面取得了高准确性.
- 该方法通过缩小维度提供了计算效率.
- 惯性张量分析为AD进展提供了新的物理见解.
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