Breast Cancer Prediction Using Data Mining Classification Techniques

被引:0
作者
Kazi, Abdul Karim [1 ]
Waseemullah [1 ]
Baig, Mirza Adnan [1 ]
Khan, Shahzaib [1 ]
机构
[1] NED Univ, Dept Comp Sci & Informat Technol, Karachi 75270, Pakistan
来源
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2022年 / 22卷 / 09期
关键词
Data Mining; Breast Cancer; Computer Vision; Deep Learning; Disease Prediction; IMAGES;
D O I
10.22937/IJCSNS.2022.22.9.91
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In today's world Breast Cancer has become the major source of mortality among women especially in underdeveloped countries like Pakistan, Sri Lanka, and Bangladesh. This is a highly alarming situation and needs the attention of the research community as there are not enough resources and health facilities. The rate of incidence could be reduced if the cancer is diagnosed at an early stage instead of late stages. Breast cancer occurs when some breast cells begin to rise abnormally. This research study intends to predict breast cancer by analyzing a set of attributes that have been selected from several classifications so that prevention can be done in time before it becomes incurable. This research work focuses on different classification techniques of data mining to predict Breast Cancer such as Decision Trees, Random Forest, Logistic Regression, Support Vector Machine, and Linear Discriminant Analysis and their comparative analysis for accurate disease detection. The dataset of breast cancer histopathology images was acquired from online recourses consisting of 277,524 images. The experimental results show that Random Forest performs better than all other algorithms used in this research study with an accuracy of 88.80 %, precision of 83.71 %, and recall 94.28 %. Python programming language to implement and perform the comparative analysis of algorithms used in this research work.
引用
收藏
页码:696 / 704
页数:9
相关论文
共 34 条
  • [1] A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine
    Abdelsalam, Mohamed M.
    Zahran, M. A.
    [J]. IEEE ACCESS, 2021, 9 : 22844 - 22858
  • [2] Abdulkareem Nasiba Mahdi., 2021, International Journal of Science and Business, V5, P128
  • [3] Ashraf M., 2021, INT C INN COMP COMM, P239
  • [4] Bengio Y., 2012, P ICML WORKSH UNS TR, V7, P19
  • [5] Bishop C. M., 2006, PATTERN RECOGN
  • [6] Bowman G. R., 2013, INTRO MARKOV STATE M
  • [7] Boyle P., 2008, World Cancer Report 2008
  • [8] A Decision Tree-Initialised Neuro-fuzzy Approach for Clinical Decision Support
    Chen, Tianhua
    Shang, Changjing
    Su, Pan
    Keravnou-Papailiou, Elpida
    Zhao, Yitian
    Antoniou, Grigoris
    Shen, Qiang
    [J]. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2021, 111 (111)
  • [9] Classification of Endomicroscopic Images of the Lung Based on Random Subwindows and Extra-Trees
    Desir, Chesner
    Petitjean, Caroline
    Heutte, Laurent
    Salauen, Mathieu
    Thiberville, Luc
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2012, 59 (09) : 2677 - 2683
  • [10] Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier
    Dhaliwal, Jasbir
    Erdman, Lauren
    Drysdale, Erik
    Rinawi, Firas
    Muir, Jennifer
    Walters, Thomas D.
    Siddiqui, Iram
    Griffiths, Anne M.
    Church, Peter C.
    [J]. JOURNAL OF PEDIATRIC GASTROENTEROLOGY AND NUTRITION, 2021, 72 (02) : 262 - 269