Different Machine Learning Algorithms for Breast Cancer Diagnosis

Breast cancer is the second most commonly diagnosed cancer in women throughout the world. It is on the rise, especially in developing countries, where the majority of cases are discovered late. Breast cancer develops when cancerous tumors form on the surface of the breast cells. The absence of accurate prognostic models to assist physicians recognize symptoms early makes it difficult to develop a treatment plan that would help patients live longer. However, machine learning techniques have recently been used to improve the accuracy and speed of breast cancer diagnosis. If the accuracy is flawless, the model will be more efficient, and the solution to breast cancer diagnosis will be better. Nevertheless, the primary difficulty for systems developed to detect breast cancer using machine-learning models is attaining the greatest classification accuracy and picking the most predictive feature useful for increasing accuracy. As a result, breast cancer prognosis remains a difficulty in today's society. This research seeks to address a flaw in an existing technique that is unable to enhance classification of continuous-valued data, particularly its accuracy and the selection of optimal features for breast cancer prediction. In order to address these issues, this study examines the impact of outliers and feature reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which was tested using seven different machine learning algorithms. The results show that Logistic Regression, Random Forest, and Adaboost classifiers achieved the greatest accuracy of 99.12%, on removal of outliers from the dataset. Also, this filtered dataset with feature selection, on the other hand, has the greatest accuracy of 100% and 99.12% with Random Forest and Gradient boost classifiers, respectively. When compared to other state-of-the-art approaches, the two suggested strategies outperformed the unfiltered data in terms of accuracy. The suggested architecture might be a useful tool for radiologists to reduce the number of false negatives and positives. As a result, the efficiency of breast cancer diagnosis analysis will be increased.

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Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis

Journal of Intelligent Systems ◽

10.1515/jisys-2020-0089 ◽

2021 ◽

Vol 30 (1) ◽

pp. 998-1013

Author(s):

Shubham Vashisth ◽

Ishika Dhall ◽

Garima Aggarwal

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector ◽

Malignant Breast ◽

Quantum Technology ◽

Classical Computer

Abstract The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score, to exemplify the supremacy of quantum SVM over its conventional variants.

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Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

Journal of Healthcare Engineering ◽

10.1155/2019/4253641 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 8

Author(s):

Habib Dhahri ◽

Eslam Al Maghayreh ◽

Awais Mahmood ◽

Wail Elkilani ◽

Mohammed Faisal Nagi

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Genetic Programming ◽

Learning Algorithm ◽

Empirical Studies ◽

Learning Algorithms ◽

Breast Cancer Diagnosis ◽

Machine Learning Algorithms ◽

Programming Technique ◽

Soft Computing Techniques

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.

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Data Mining and Machine Learning Approaches in Breast Cancer Biomedical Research

Advances in Business Information Systems and Analytics - Sentiment Analysis and Knowledge Discovery in Contemporary Business ◽

10.4018/978-1-5225-4999-4.ch011 ◽

2019 ◽

pp. 175-204

Author(s):

Gunavathi Chellamuthu ◽

Kannimuthu S. ◽

Premalatha K.

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Data Mining ◽

Cancer Diagnosis ◽

Biomedical Research ◽

Breast Cancer Diagnosis ◽

Machine Learning Algorithms ◽

Cancer Prognosis ◽

Whole Process ◽

Breast Lumps

Breast cancer is the most common invasive cancer in females worldwide. Breast cancer diagnosis and breast cancer prognosis are the two important challenges for the researchers in the medical field and also for the practitioners. If the cells in the breast start to grow without any control, it leads to cancer. Normally, the growth of the lump can be seen using x-ray. The benign and malignant breast lumps are distinguished during breast cancer diagnosis. The prognosis process predicts the period at which the breast cancer is likely to reappear in patients who have had their cancers removed. Data mining techniques and machine learning algorithms are mostly used in the whole process of breast cancer diagnosis and treatment. They utilize the large volume of breast cancer data for extracting knowledge. The application of data mining and machine learning methods in biomedical research is presently vital and crucial in efforts to transform intelligently all available data into valuable knowledge.

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