Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.

Habib Dhahri,Eslam Al Maghayreh,Mohammed Faisal Nagi,Wail Elkilani,Awais Mahmood

doi:10.1155/2019/4253641

Habib Dhahri, Eslam Al Maghayreh + Show 3 more

Open Access

https://doi.org/10.1155/2019/4253641

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Abstract

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

Highlights

Breast cancer is a prevalent cause of death, and it is the only type of cancer that is widespread among women worldwide [1]
In the last few decades, several data mining and machine learning techniques have been developed for breast cancer detection and classification [5,6,7], which can be divided into three main stages: preprocessing, feature extraction, and classification
With the aim of preventing the overfitting, the cross-validation is a powerful concept against this problem

Summary

Introduction

Breast cancer is a prevalent cause of death, and it is the only type of cancer that is widespread among women worldwide [1]. Many imaging techniques have been developed for early detection and treatment of breast cancer and to reduce the number of deaths [2], and many aided breast cancer diagnosis methods have been used to increase the diagnostic accuracy [3, 4]. In the last few decades, several data mining and machine learning techniques have been developed for breast cancer detection and classification [5,6,7], which can be divided into three main stages: preprocessing, feature extraction, and classification. To facilitate interpretation and analysis, the preprocessing of mammography films helps improve the visibility of peripheral areas and intensity distribution, and several methods have been reported to assist in this process [8, 9]. Feature extraction is an important step in breast cancer detection because it helps discriminate between benign and malignant tumors. Image properties such as smoothness, coarseness, depth, and regularity are extracted by segmentation [10]

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