基于Logistic模型的人体细菌密度数据分析与优化

摘要

目前,随着智能医疗器械的快速发展,日常生活中,医疗器械的PCT (Procalcitonin)测试仪被人们越来越广泛的使用;PCT测试仪主要是用来检测人体感染细菌的有效工具,它自身带有CMOS摄像头,用来采集沾有病毒的试剂卡条上的图像数据;得到若干组的图像数据,并有与之对应的光密度值;由于PCT测试仪采集到的数据处理方面是否能够精确地计算出人体感染细菌密度,是当前值得关注的问题,故在该论文中,采用机器学习的Logistic分析方法,做出详细的数据处理与优化,以此达到精确计算出人体感染细菌密度的目的;首先构造一个经典的Logistic模型,对采集的数据进行处理分析,并使用遗传算法(Genetic Algorithm,GA)对该模型进行计算,以此得出全局最优解.%At present,with the rapid development of intelligent medical devices,people's daily lives on the medical device PCT (Procalcitonin) test instrument is more and more widely used.PCT tester is mainly used to detect the human body infected with bacteria effective method.PCT test instrument itself with a CMOS camera,used to capture the human body infected with the virus of the reagent card on the image data.Several groups of image data are obtained,and the corresponding optical density values are obtained.As the problem is that the PCT tester can accurately calculate the bacterial density of the human body in the collected data processing,the Logistic analysis method of machine learning is used to make detailed data processing and optimization,in order to accurately calculate the human body density of bacterial infection.A classical Logistic model is constructed,and the collected data are processed and analyzed,and the Genetic Algorithm (GA) is used to calculate the global optimal solution.