A support vector machine approach to breast cancer diagnosis and prognosis

被引：0
作者：
Zafiropoulos, Elias [1 ]
Maglogiannis, Ilias [1 ]
Anagnostopoulos, Ioannis [1 ]
机构：
[1] Univ Aegean, Dept Informat & Commun Syst Engn, GR-83200 Karlovassi, Samos, Greece
来源：
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS | 2006年 / 204卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning. The paper presents a Support Vector Machine (SVM) approach for the prognosis and diagnosis of breast cancer implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and the Wisconsin Prognostic Breast Cancer (WPBC) datasets found in literature. The SVM algorithm performs excellently in both problems for the case study datasets, exhibiting high accuracy, sensitivity and specificity indices.
引用
收藏
页码：500 / +
页数：2
相关论文
共 50 条
  • [31] A Latent Space Support Vector Machine (LSSVM) Model for Cancer Prognosis
    Ford, William
    Land, Walker
    COMPLEX ADAPTIVE SYSTEMS, 2014, 36 : 470 - 475
  • [32] Support vector machine for diagnosis cancer disease: A comparative study
    Sweilam, Nasser H.
    Tharwat, A. A.
    Moniem, N. K. Abdel
    EGYPTIAN INFORMATICS JOURNAL, 2010, 11 (02) : 81 - 92
  • [33] Optical diagnosis of colon and cervical cancer by support vector machine
    Mukhopadhyay, Sabyasachi
    Kurmi, Indrajit
    Dey, Rajib
    Das, Nandan K.
    Pradhan, Sanjay
    Pradhan, Asima
    Ghosh, Nirmalya
    Panigrahi, Prasanta K.
    Mohanty, Samarendra
    BIOPHOTONICS: PHOTONIC SOLUTIONS FOR BETTER HEALTH CARE V, 2016, 9887
  • [34] Breast cancer malignancy identification using support vector machine
    Sawarkar, Sudhir D.
    Ghatol, Ashok A.
    WSEAS Transactions on Computers, 2006, 5 (08): : 1707 - 1712
  • [35] Fuzzy Support Vector Machine for Breast Cancer Gene Classification
    Congqin-Yi
    Zhou, Ruyan
    Hu, Kening
    2017 2ND INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC 2017), 2017, : 676 - 679
  • [36] Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics
    Tatjana Dramićanin
    Lea Lenhardt
    Ivana Zeković
    Miroslav D. Dramićanin
    Journal of Fluorescence, 2012, 22 : 1281 - 1289
  • [37] An effective classification approach to categorize the breast cancer using modified support vector machine as a classifier
    Muvva, Vijaya Bhaskar Reddy
    Lal, N. Dayanand
    Shah, Syed Mehr Ali
    INTERNET TECHNOLOGY LETTERS, 2024, 7 (06)
  • [38] Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics
    Dramicanin, Tatjana
    Lenhardt, Lea
    Zekovic, Ivana
    Dramicanin, Miroslav D.
    JOURNAL OF FLUORESCENCE, 2012, 22 (05) : 1281 - 1289
  • [39] An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine
    Wang, Mozhi
    Pang, Zhiyuan
    Wang, Yusong
    Cui, Mingke
    Yao, Litong
    Li, Shuang
    Wang, Mengshen
    Zheng, Yanfu
    Sun, Xiangyu
    Dong, Haoran
    Zhang, Qiang
    Xu, Yingying
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [40] Classification of Mass Type Based on Segmentation Techniques with Support Vector Machine Model for Diagnosis of Breast cancer
    Makandar, Aziz
    Halalli, Bhagirathi
    2017 1ST IEEE INTERNATIONAL CONFERENCE ON DATA MANAGEMENT, ANALYTICS AND INNOVATION (ICDMAI), 2017, : 81 - 86
12345