Analysis of Efficiency of Classification and Prediction Algorithms (Naive Bayes) for Breast Cancer Dataset

被引:0
|
作者
Rashmi, G. D. [1 ]
Lekha, A. [1 ]
Bawane, Neelam [1 ]
机构
[1] PES Inst Technol, Comp Applicat, Bangalore, Karnataka, India
来源
2015 INTERNATIONAL CONFERENCE ON EMERGING RESEARCH IN ELECTRONICS, COMPUTER SCIENCE AND TECHNOLOGY (ICERECT) | 2015年
关键词
Benign; Breast Cancer; Classification; Malignant; Prediction;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In developed countries death of women due to breast cancer has become regular. Data mining techniques are used to provide the analysis for the classification and prediction algorithms. The algorithms used here are Naive Bayes classification algorithm and Naive Bayes prediction algorithm. The algorithms are used to classify and predict whether the tumour is either benign or malignant. The data used in the algorithms is taken from the Wisconsin University database. Data sets are used to find the success rate and the error rate.
引用
收藏
页码:108 / 113
页数:6
相关论文
共 50 条
  • [31] Comparative analysis of classification algorithms on the breast cancer recurrence using machine learning
    Valentina Mikhailova
    Gholamreza Anbarjafari
    Medical & Biological Engineering & Computing, 2022, 60 : 2589 - 2600
  • [32] TURNOVER PREDICTION IN A CALL CENTER: BEHAVIORAL EVIDENCE OF LOSS AVERSION USING RANDOM FOREST AND NAIVE BAYES ALGORITHMS
    Valle, Mauricio A.
    Ruz, Gonzalo A.
    APPLIED ARTIFICIAL INTELLIGENCE, 2015, 29 (09) : 923 - 942
  • [33] 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
  • [34] Empirical Analysis on Cancer Dataset with Machine Learning Algorithms
    Vital, T. PanduRanga
    Krishna, M. Murali
    Narayana, G. V. L.
    Suneel, P.
    Ramarao, P.
    SOFT COMPUTING IN DATA ANALYTICS, SCDA 2018, 2019, 758 : 789 - 801
  • [35] Cancer Disease Prediction Using Naive Bayes,K-Nearest Neighbor and J48 algorithm.
    Maliha, Shanjida Khan
    Ema, Romana Rahman
    Ghosh, Simanta Kumar
    Ahmed, Helal
    Mollick, Md. Rafsun Jony
    Islam, Tajul
    2019 10TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT), 2019,
  • [36] Magnetic resonance imaging texture analysis classification of primary breast cancer
    Waugh, S. A.
    Purdie, C. A.
    Jordan, L. B.
    Vinnicombe, S.
    Lerski, R. A.
    Martin, P.
    Thompson, A. M.
    EUROPEAN RADIOLOGY, 2016, 26 (02) : 322 - 330
  • [37] Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis
    Asri, Hiba
    Mousannif, Hajar
    Al Moatassime, Hassan
    Noel, Thomas
    7TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2016) / THE 6TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2016) / AFFILIATED WORKSHOPS, 2016, 83 : 1064 - 1069
  • [38] Performance Analysis of Segmentation Algorithms for the Detection of Breast Cancer
    Aswathy, M. A.
    Jagannath, M.
    INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE, 2020, 167 : 666 - 676
  • [39] Breast Cancer Classification using Decision Tree Algorithms
    Tarawneh, Omar
    Otair, Mohammed
    Husni, Moath
    Abuaddous, Hayfa Y.
    Tarawneh, Monther
    Almomani, Malek A.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (04) : 676 - 680
  • [40] Breast Cancer Factors Detection Using Classification Algorithms
    Chow, Sook Theng
    Leow, Yi Qian
    Ching, Feiyau
    Damanhoori, Faten
    Singh, Manmeet Mahinderjit
    INNOVATION MANAGEMENT AND SUSTAINABLE ECONOMIC COMPETITIVE ADVANTAGE: FROM REGIONAL DEVELOPMENT TO GLOBAL GROWTH, VOLS I - VI, 2015, 2015, : 1786 - 1801