Abstract
Breast Cancer is the second most dangerous cancer in the world. Most of the women die due to breast cancer not only in India but everywhere in the world. In 2011, the USA stated that one in eight women suffered from cancer. Breast cancer develops due to the abnormal cell division in the breast itself which results in the formation of either benign or malignant cancer. So, it is very important to predict breast cancer at an early stage and by providing proper treatment, many lives can be saved. This paper aims to give a comparative study by applying different machine learning algorithms such as Support Vector Machine, K-Nearest Neighbour, Naïve Bayes, Decision Tree, K-means, and Artificial Neural Networks on Wisconsin Diagnostic dataset to predict breast cancer at an early stage.
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Author Keywords
- An artificial neural network,
- breast cancer,
- support vector machine,
- human disease
Introduction
Cancer which develops in the breast is called Breast Cancer and is the second most dangerous cancer in the world. It is responsible for the death of many women around the globe. More than thirteen thousand Indians die every day due to cancer, according to the National Cancer Registry Programme of the India Council of Medical Research (ICMR). Between 2012 and 2014, the mortality rate due to cancer increased by approximately 6% [1]. Most doctors do the biopsy in order to check whether the patient has cancer or not and whether it is benign or malignant. Cancer that is benign can be said to be “non-cancerous cancer” as it does not spread to other parts of the body whereas a malignant cancer is fatal as it spreads throughout the body and is uncontrollable. The cure to cancer has not been found yet. The only way to save a person’s life suffering from it is to remove that portion of the body that has been affected. The best way is to save a person from even developing cancer, and that can be achieved by a timely diagnosis. Machine Learning is a technique that can learn and retrieve information from data and use that ‘gained’ experience to predict the required outcomes. Machine Learning algorithms have provided great assistance in many fields and early-stage cancer prediction. In computer science, machine learning can be classified in three different ways as supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, labeled data is available, and based on that labeled data, the machine predicts the label of the unlabelled input features, whereas, in unsupervised learning, all the features are available without any label or output class. In the case of reinforcement learning, it is the technique of letting the models learn on their own. In this type of learning the machine performs a specific task and either rewards or penalizes itself based on a set of defined rules, and mainly focuses on maximizing the total reward. There are several machine learning techniques that can help to predict whether the person affected is having a benign or malignant cancer, and this process would be efficient and without any errors. In this paper, the authors used a total of six machine learning algorithms to predict breast cancer. The further sections are as follows: section 2 describes the existing literature work done by other researchers. Section 3 explains the methodology of the implemented work. Section 4 describes the used dataset with experiment results. Section 5 concludes the work with future scope.
Conclusion
Breast Cancer is fatal and needs early detection in order to be cured. This year, an estimated 268,600 women in the United States will be diagnosed with invasive breast cancer, and 62,930 women will be diagnosed with in situ breast cancer. An estimated 2,670 men in the United States will be diagnosed with breast cancer. So, prediction techniques are necessary to achieve the detection of cancer. To predict breast cancer in an advanced stage, authors applied six different machine learning algorithms as DT, NB, LR, RF, SVM, and NN on the Wisconsin Breast Cancer dataset which is publicly available on the internet. After comparing all the applied algorithm, the authors conclude that artificial neural network gives better prediction as 97.85% compared to all other algorithms. The best accuracy is given by ANN and hence can be used to predict cancer and save lives at present as well as in the future. This accuracy can be improved by increasing data size in the future.
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FULL Paper PDF file:
Comparative Analysis to Predict Breast Cancer using Machine Learning Algorithms: A SurveyBibliography
author
Year
2020
Title
2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 192-196
Publish in
Comparative Analysis to Predict Breast Cancer using Machine Learning Algorithms: A Survey,
Doi
10.1109/ICICT48043.2020.9112464.
PDF reference and original file: Click here
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
