Breast Cancer Detection Using Machine Learning Algorithms

被引：0
|
作者：
Sharma, Shubham [1 ]
Aggarwal, Archit [2 ]
Choudhury, Tanupriya [1 ]
机构：
[1] UPES, Dept Informat, Sch Comp Sci, Dehra Dun, Uttar Pradesh, India
[2] Amity Univ Uttar Pradesh, Noida, Uttar Pradesh, India
来源：
PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON COMPUTATIONAL TECHNIQUES, ELECTRONICS AND MECHANICAL SYSTEMS (CTEMS) | 2018年
关键词：
Breast Cancer; random forest; k-Nearest-Neighbor; naive bayes;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The most frequently occuring cancer among Indian women is breast cancer. There is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women[1]. This paper aims to present comparison of the largely popular machine learning algorithms and techniques commonly used for breast cancer prediction, namely Random Forest, kNN (k-Nearest-Neighbor) and Naive Bayes. The Wisconsin Diagnosis Breast Cancer data set was used as a training set to compare the performance of the various machine learning techniques in terms of key parameters such as accuracy, and precision. The results obtained are very competitive and can be used for detection and treatment.
引用
收藏
页码：114 / 118
页数：5
相关论文
共 50 条
  • [1] Survey of machine learning algorithms for breast cancer detection using mammogram images
    Meenalochini, G.
    Ramkumar, S.
    MATERIALS TODAY-PROCEEDINGS, 2021, 37 : 2738 - 2743
  • [2] Prediction of Breast Cancer using Machine Learning Algorithms
    Mangal, Anuj
    Jain, Vinod
    PROCEEDINGS OF THE 2021 FIFTH INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC 2021), 2021, : 464 - 466
  • [3] Using Machine Learning Algorithms for Breast Cancer Diagnosis
    El-Lamey, Mazen Mobtasem
    Eid, Mohab Mohammed
    Gamal, Muhammad
    Bishady, Nour-Elhoda Mohamed
    Mohamed, Ali Wagdy
    INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING, 2021, 12 (04) : 117 - 154
  • [4] Breast Cancer Detection Using Machine Learning
    Sivasangari, A.
    Ajitha, P.
    Bevishjenila
    Vimali, J. S.
    Jose, Jithina
    Gowri, S.
    MOBILE COMPUTING AND SUSTAINABLE INFORMATICS, 2022, 68 : 693 - 702
  • [5] Predicting the recurrence of breast cancer using machine learning algorithms
    Alzu'bi, Amal
    Najadat, Hassan
    Doulat, Wesam
    Al-Shari, Osama
    Zhou, Leming
    MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (09) : 13787 - 13800
  • [6] Classification of Breast Cancer Data Using Machine Learning Algorithms
    Akbugday, Burak
    2019 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO), 2019, : 429 - 432
  • [7] Predicting the recurrence of breast cancer using machine learning algorithms
    Amal Alzu’bi
    Hassan Najadat
    Wesam Doulat
    Osama Al-Shari
    Leming Zhou
    Multimedia Tools and Applications, 2021, 80 : 13787 - 13800
  • [8] Prediction of Breast Cancer Using Simple Machine Learning Algorithms
    Devi, Seeta
    Dumbre, Dipali
    Chavan, Ranjana
    2024 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND APPLIED INFORMATICS, ACCAI 2024, 2024,
  • [9] Breast Cancer: Classification of Tumors Using Machine Learning Algorithms
    Hettich, David
    Olson, Megan
    Jackson, Andie
    Kaabouch, Naima
    2021 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND VIRTUAL ENVIRONMENTS FOR MEASUREMENT SYSTEMS AND APPLICATIONS (IEEE CIVEMSA 2021), 2021,
  • [10] A comparative survey of Machine Learning classification Algorithms for Breast Cancer Detection
    Kaklamanis, Markos Marios
    Filippakis, Michael E.
    PROCEEDINGS OF THE 23RD PAN-HELLENIC CONFERENCE OF INFORMATICS (PCI 2019), 2019, : 97 - 103
12345