Application of Machine Learning techniques to improve detection, diagnosis & prediction of breast cancer: A Comparative Analysis

Shobhit Shrotriya, Nizar Banu P K, Avi Kulkarni
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Abstract

There is an increasing cancer burden in India across ages and sexes. The most significant cancer incident rate in females is ‘Breast Cancer’. Early detection and treatment are the key to lower mortality rate and better survival rates for cancer patients in the country. This review paper provides an understanding of the various types of breast cancers, their symptoms, causes, current detection and diagnosis methods. The paper presents different Machine Learning (ML) techniques that are in development for the detection and diagnosis of breast cancer. The objective of the paper is to highlight outcomes of some select previous studies between 2016 to 2020 using various ML techniques and summarize the selected algorithms which can be used for breast cancer prediction and diagnosis. In our paper, we have also made an attempt to implement the Convolutional Neural Networks (CNN) model on the Breast Cancer Wisconsin (Diagnostic) dataset, whose results are presented and discussed.
应用机器学习技术改进乳腺癌的检测、诊断和预测:比较分析
在印度,不同年龄和性别的癌症负担都在不断加重。女性癌症发病率最高的是 "乳腺癌"。早期发现和治疗是降低死亡率和提高印度癌症患者生存率的关键。本综述论文介绍了各种类型的乳腺癌、其症状、病因、目前的检测和诊断方法。本文介绍了正在开发的用于检测和诊断乳腺癌的各种机器学习(ML)技术。本文旨在重点介绍 2016 年至 2020 年期间使用各种 ML 技术进行的一些精选研究成果,并总结可用于乳腺癌预测和诊断的精选算法。在本文中,我们还尝试在乳腺癌威斯康星(诊断)数据集上实施卷积神经网络(CNN)模型,并对其结果进行了介绍和讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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