A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis

被引:31
作者
Adebiyi, Marion Olubunmi [1 ]
Arowolo, Micheal Olaolu [2 ]
Mshelia, Moses Damilola [1 ]
Olugbara, Oludayo O. [3 ]
机构
[1] Landmark Univ, Coll Pure & Appl Sci, Dept Comp Sci, Omu Aran 251103, Nigeria
[2] Univ Missouri, Bond Life Sci Ctr, Dept Elect Engn & Comp Sci, Columbia, MO 65211 USA
[3] Durban Univ Technol, MICT SETA 4IR Ctr Excellence, ZA-4000 Durban, South Africa
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 22期
关键词
breast cancer; classification model; discriminant analysis; random forest; support vectors;
D O I
10.3390/app122211455
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Although most cases are identified at a late stage, breast cancer is the most public malignancy amongst women globally. However, mammography for the analysis of breast cancer is not routinely available at all general hospitals. Prolonging the period between detection and treatment for breast cancer may raise the likelihood of proliferating the disease. To speed up the process of diagnosing breast cancer and lower the mortality rate, a computerized method based on machine learning was created. The purpose of this investigation was to enhance the investigative accuracy of machine-learning algorithms for breast cancer diagnosis. The use of machine-learning methods will allow for the classification and prediction of cancer as either benign or malignant. This investigation applies the machine learning algorithms of random forest (RF) and the support vector machine (SVM) with the feature extraction method of linear discriminant analysis (LDA) to the Wisconsin Breast Cancer Dataset. The SVM with LDA and RF with LDA yielded accuracy results of 96.4% and 95.6% respectively. This research has useful applications in the medical field, while it enhances the efficiency and precision of a diagnostic system. Evidence from this study shows that better prediction is crucial and can benefit from machine learning methods. The results of this study have validated the use of feature extraction for breast cancer prediction when compared to the existing literature.
引用
收藏
页数:15
相关论文
共 38 条
[1]   A new nested ensemble technique for automated diagnosis of breast cancer [J].
Abdar, Moloud ;
Zomorodi-Moghadam, Mariam ;
Zhou, Xujuan ;
Gururajan, Raj ;
Tao, Xiaohui ;
Barua, Prabal D. ;
Gururajan, Rashmi .
PATTERN RECOGNITION LETTERS, 2020, 132 :123-131
[2]   On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset [J].
Agarap, Abien Fred M. .
2ND INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING (ICMLSC 2018), 2015, :5-9
[3]  
Aishwarja A I., 2020, International conference on intelligent computing and optimization, P546
[4]   A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications [J].
Ak, Muhammet Fatih .
HEALTHCARE, 2020, 8 (02)
[5]  
Akram M, 2017, BIOL RES, V50
[6]  
[Anonymous], 2016, INT J PUBLIC HLTH SC, DOI DOI 10.11591/IJPHS.V5I3.4787
[7]  
[Anonymous], 2015, Advances in Artificial Neural Systems, DOI DOI 10.1155/2015/265637
[8]  
Arowolo M.O., 2021, Walailak J of Sci Technol, V18, P9849, DOI 10.48048/wjst.2021.9849
[9]   Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis [J].
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
[10]  
Awad M., 2015, Efficient Learning Machines, P39, DOI DOI 10.1007/978-1-4302-5990-93