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通过基于多层感知神经网络的光谱子集特征选择来识别癌症风险,以提前治疗

M Ramkumar¹, P Shanmugaraja², V Anusuya³

¹Department of CSBS, Knowledge Institute of Technology, Salem, Tamil Nadu, India.

Computer methods in biomechanics and biomedical engineering

|October 4, 2023

概括

这项研究引入了一种新的子集集群基于特征选择,使用多层感知神经网络 (SCFS-MLPNN) 准确预测癌症风险. 该方法通过提高分类准确性来提高早期癌症检测.

科学领域:

生物医学研究的研究.
计算生物学是一种计算生物学.
机器学习在医疗保健中的应用

背景情况:

癌症是一个重大的全球健康挑战.
准确预测癌症风险对于早期检测和干预至关重要.
特征选择和分类是癌症风险分析中的关键挑战.

研究的目的:

为癌症风险预测使用多层感知神经网络 (SCFS-MLPNN) 提出一种新的子集集群基于特征选择.
为了提高早期癌症检测的分类准确性.
识别和利用关系特征来改进风险分析.

主要方法:

使用密集的相互疾病影响率 (IMDIR) 和连续疾病模式刺激率 (SDPSR) 进行预处理,以确定关系特征和模式.
使用跨类子空间聚类 (ICSSC) 和光谱子集特征选择 (SSFS) 进行特征选择和聚类.
使用在选定的子集特征上训练的多层感知神经网络 (MLPNN) 进行分类.

主要成果:

拟议的SCFS-MLPNN方法实现了91.8%的风险分析准确度.
获得了91.3%的F测量,表明高精度和回忆.
与以前的方法相比,该方法显示出更高的分类准确性.

结论:

在SCFS-MLPNN有效地利用子集特征通过关系特征集群来改善癌症风险分类.
拟议的方法显示了早期癌症检测和诊断的重大前景.
取得的高精度支持其在早期癌症风险评估中临床应用的建议.

关键词:

功能选择功能选择癌症预测癌症预测这是分类分类的分类.神经网络的神经网络的神经网络分析模式分析模式分析模式

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