Data Mining - Concepts and Techniques.

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Background: Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems. Objective: This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks. Methods: In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets. Results: The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems. Conclusions: From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

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Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

An improved random forest-based rule extraction method for breast cancer diagnosis

is a big issue all around the world. It is a disease, which is fatal in many cases and has affected the lives of many and will continue to affect the lives of many more. Breast cancer represents the second primary cause of cancer deaths in women today and has become the most common cancer among women both in the developed and the developing world in the last years. 40,000 women die in a year from this disease, which is one woman every 13 minute dying from this disease everyday. Early detection of breast cancer is far easier to cure. This paper presents a decision tree based data mining technique for early detection of breast cancer. Breast cancer diagnosis differentiates benign (lacks ability to invade neighboring tissue) from malignant (ability to invade neighboring tissue) breast tumors. This paper also discusses various data mining approaches that have been utilized for breast cancer diagnosis, and also summarizes breast cancer in general (types, risk factors, symptoms and treatment).

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N. Vishnusri

Papers

Diagnosis of Breast Cancer using Decision Tree Data Mining Technique