Breast cancer prediction: A comparative study using machine learning techniques

Islam, Md. Milon and Haque, Md. Rezwanul and Iqbal, Hasib and Hasan, Md. Munirul and Hasan, Mahmudul and Kabir, Muhammad Nomani (2020) Breast cancer prediction: A comparative study using machine learning techniques. SN Computer Science, 1 (5). pp. 1-14. ISSN 2662-995X. (Published)

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Abstract

Early detection of disease has become a crucial problem due to rapid population growth in medical research in recent times. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. Breast cancer is the second most severe cancer among all of the cancers already unveiled. An automatic disease detection system aids medical staffs in disease diagnosis and offers reliable, effective, and rapid response as well as decreases the risk of death. In this paper, we compare five supervised machine learning techniques named support vector machine (SVM), K-nearest neighbors, random forests, artificial neural networks (ANNs) and logistic regression. The Wisconsin Breast Cancer dataset is obtained from a prominent machine learning database named UCI machine learning database. The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value, false-negative rate, false-positive rate, F1 score, and Matthews Correlation Coefficient. Additionally, these techniques were appraised on precision–recall area under curve and receiver operating characteristic curve. The results reveal that the ANNs obtained the highest accuracy, precision, and F1 score of 98.57%, 97.82%, and 0.9890, respectively, whereas 97.14%, 95.65%, and 0.9777 accuracy, precision, and F1 score are obtained by SVM, respectively.

Item Type:	Article
Uncontrolled Keywords:	Breast cancer prediction; Cancer dataset; Machine learning; Support vector machine; Random forests; Artificial neural networks; K-nearest neighbors; Logistic regression
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty/Division:	Faculty of Computer System And Software Engineering Institute of Postgraduate Studies
Depositing User:	Dr. Muhammad Nomani Kabir
Date Deposited:	17 Jun 2021 07:29
Last Modified:	17 Jun 2021 07:29
URI:	http://umpir.ump.edu.my/id/eprint/29306
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