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为了解决当前疾病模式识别过程存在的精度低,速度慢等缺陷,设计了一种基于激光荧光光谱数据的疾病模式识别方法。首先收集激光荧光光谱数据,并对其进行消噪和降维处理,然后基于处理后的激光荧光光谱数据建立疾病模式识别的分类器,最后采用疾病模式识别的实验对本文方法的有效性进行测试,其疾病模式识别的精度高达95%以上,并与其它识别方法进行对比实验,本文方法的疾病模式识别结果具有十分明显的优势,实际应用价值更高。
In order to solve the shortcomings of low accuracy and slow speed in current disease pattern recognition, a novel disease pattern recognition method based on laser fluorescence spectroscopy data was designed. Firstly, the data of laser fluorescence spectra were collected, denoised and dimensionally reduced, and then the classifier of disease pattern recognition was established based on the processed laser fluorescence spectroscopy data. Finally, the effectiveness of the method in this paper was tested by the experiment of disease pattern recognition , The accuracy of disease pattern recognition is more than 95%, and compared with other identification methods. The results of disease pattern recognition in this paper have obvious advantages and have higher practical value.