Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Author:

Aamir Sanam1ORCID,Rahim Aqsa2ORCID,Aamir Zain3ORCID,Abbasi Saadullah Farooq4ORCID,Khan Muhammad Shahbaz5ORCID,Alhaisoni Majed6ORCID,Khan Muhammad Attique67ORCID,Khan Khyber8ORCID,Ahmad Jawad9ORCID

Affiliation:

1. Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan

2. Faculty of Science and Technology, University of Tromsø, Tromso, Norway

3. Department of Data Science, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan

4. Department of Electrical Engineering, National University of Technology, Islamabad 44000, Pakistan

5. Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan

6. Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia

7. Department of Computer Science, HITEC University, Taxila, Pakistan

8. Department of Computer Science, Khurasan University, Jalalabad, Afghanistan

9. School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK

Abstract

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

Reference44 articles.

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