Analysis of breast cancer event logs using various regression techniques

被引：1
|
作者：
Saravanan, M. S. [1 ]
Patil, Pradnya [2 ]
Subbaiah, K. Venkata [3 ]
机构：
[1] Saveetha Sch Engn, Saveetha Inst Med & Tech Sci, Dept Artificial Intelligence, Chennai, Tamil Nadu, India
[2] KJ Somaiya Inst Engn & Informat Technol, Dept Comp Engn, Mumbai, Maharashtra, India
[3] DRK Institue Sci & Technol, Dept Comp Sci & Engn, Hyderabad, India
来源：
2021 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI) | 2021年
关键词：
Breast cancer; Machine Learning; Healthcare; Event logs; Regression technique;
D O I：
10.1109/ICCCI50826.2021.9402360
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The breast cancer is a chronic disorder that causes serious illness to the patients despite their age groups. Breast cancer has more number of research to identify the root causes. But in recent research finding also concentrated more on factors affecting the breast cancer with different type of event logs, such as healthcare centers generated data and trail data taken from various webpages. The machine learning techniques are mostly applied on complex type of event logs such as cancer data set, brain tumor dataset and heart related diseases. Among various diseases breast cancer is the one has more complex event logs, which is very complex to analyze and to find the root causes. This research article discuss about the breast cancer data set using logistic regression technique applied with python programming language. This paper also deals about the root causes about the breast cancer and related issues.
引用
收藏
页数：4
相关论文
共 50 条
  • [31] USB Artifact Analysis Using Windows Event Viewer, Registry and File System Logs
    Neyaz, Ashar
    Shashidhar, Narasimha
    ELECTRONICS, 2019, 8 (11)
  • [32] Machine Learning techniques for Prediction from various Breast Cancer Datasets
    Shalini, M.
    Radhika, S.
    2020 SIXTH INTERNATIONAL CONFERENCE ON BIO SIGNALS, IMAGES, AND INSTRUMENTATION (ICBSII), 2020,
  • [33] Analysis of Mammography Techniques for Breast Cancer Detection
    Diderot, P. Kumaraguru
    Vasudevan, N.
    Prakash, V. R.
    2019 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET 2019): ADVANCING WIRELESS AND MOBILE COMMUNICATIONS TECHNOLOGIES FOR 2020 INFORMATION SOCIETY, 2019, : 127 - 130
  • [34] Using Event Logs for Local Correction of Process Models
    Mitsyuk A.A.
    Lomazova I.A.
    van der Aalst W.M.P.
    Automatic Control and Computer Sciences, 2017, 51 (7) : 709 - 723
  • [35] Using Event Logs and the ψ-theory to Analyse Business Processes
    Pinto, Pedro Linares
    Mendes, Carlos
    da Silva, Miguel Mira
    Caetano, Artur
    30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II, 2015, : 1195 - 1202
  • [36] iBelt ： An interpretable cluster analysis method for event logs
    Liu W.
    Wang G.
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2022, 28 (10): : 3175 - 3186
  • [37] A META REGRESSION ANALYSIS OF UTILITY WEIGHTS FOR BREAST CANCER
    Gong, J. R.
    Bae, S.
    Lim, J.
    VALUE IN HEALTH, 2016, 19 (07) : A348 - A348
  • [38] Analysis and interpretation of findings using multiple regression techniques
    Hoyt, William T.
    Leierer, Stephen
    Millington, Michael J.
    REHABILITATION COUNSELING BULLETIN, 2006, 49 (04) : 223 - 233
  • [39] Repairing Event Logs Using Timed Process Models
    Rogge-Solti, Andreas
    Mans, Ronny S.
    van der Aalst, Wil M. P.
    Weske, Mathias
    ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2013 WORKSHOPS, 2013, 8186 : 705 - 708
  • [40] Comparative Analysis of Pattern Mining Algorithms for Event Logs
    Gasimov, Orkhan
    Vaarandi, Risto
    Pihelgas, Mauno
    2023 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE, CSR, 2023, : 1 - 7
12345