Modeling and Analyzing of Breast Tumor Deterioration Process with Petri Nets and Logistic Regression

作     者：Xuyue Wang Wangyang Yu Zeyuan Ding Xiaojun Zhai Sangeet Saha 

作者机构：Key Laboratory of Intelligent Computing and Service Technology for Folk SongMinistry of Culture and Tourismand School of Computer ScienceShaanxi Normal UniversityXi’an 710100China School of Computer ScienceShaanxi Normal UniversityXi’an 710100China School of Computer Science and Electronic EngineeringUniversity of EssexColchesterCO43SQUK 

出 版 物：《Complex System Modeling and Simulation》 (复杂系统建模与仿真（英文）)

年 卷 期：2022年第2卷第3期

页      面：264-272页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：This work was supported in part by the Natural Science Foundation of Shaanxi Province(No.2021JM-205) the Fundamental Research Funds for the Central Universities 

主　　题：coloured Petri nets visual modeling machine learning breast cancer 

摘      要：It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease *** the occurrence of the disease is caused by many factors,and the pathogenesis process is also *** is necessary to use interpretable and visual modeling methods to characterize this complex *** learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic ***,in most cases,it is *** formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent,and can well visualize the analysis *** Petri Nets(CPN)is a powerful formal *** paper presents a modeling approach with CPN and machine learning in breast cancer,which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of *** evaluating the performance of several common machine learning algorithms,we finally choose the logistic regression algorithm to analyze the data,and integrate the obtained prediction model into the CPN *** method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.