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Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Aamir, Sanam; Rahim, Aqsa; Aamir, Zain; Abbasi, Saadullah Farooq; Khan, Muhammad Shahbaz; Alhaisoni, Majed; Khan, Muhammad Attique; Khan, Khyber; Ahmad, Jawad

Authors

Sanam Aamir

Aqsa Rahim

Zain Aamir

Saadullah Farooq Abbasi

Muhammad Shahbaz Khan

Majed Alhaisoni

Muhammad Attique Khan

Khyber Khan



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.

Journal Article Type Article
Acceptance Date Jul 28, 2022
Online Publication Date Aug 16, 2022
Publication Date 2022
Deposit Date Aug 22, 2022
Publicly Available Date Aug 22, 2022
Journal Computational and Mathematical Methods in Medicine
Print ISSN 1748-670X
Electronic ISSN 1748-6718
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2022
Article Number 5869529
DOI https://doi.org/10.1155/2022/5869529
Public URL http://researchrepository.napier.ac.uk/Output/2898213

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