Diagnosis of Breast Cancer using Decision Tree Data Mining Technique
TLDR
This paper presents a decision tree based data mining technique for early detection of breast cancer and discusses various data mining approaches that have been utilized for breast cancer diagnosis, and summarizes breast cancer in general.Abstract:
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).read more
Citations
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Journal ArticleDOI
Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis
TL;DR: An overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs) and their applications in BC diagnosis and prognosis is provided.
Journal ArticleDOI
Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review
TL;DR: 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.
Journal ArticleDOI
Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.
Rasheed Omobolaji Alabi,Mohammed Elmusrati,Iris Sawazaki-Calone,Luiz Paulo Kowalski,Caj Haglund,Ricardo D. Coletta,Antti Mäkitie,Tuula Salo,Alhadi Almangush,Ilmo Leivo +9 more
TL;DR: The findings show that the best classification accuracy was achieved with the boosted decision tree algorithm, which outperformed the DOI-based approach and with few parameters identified in the PFI analysis, ML technique still showed the ability to predict locoregional recurrence.
Journal ArticleDOI
An improved random forest-based rule extraction method for breast cancer diagnosis
TL;DR: Improved Random Forest-based rule extraction (IRFRE) method is developed to derive accurate and interpretable classification rules from a decision tree ensemble for breast cancer diagnosis and can be popularized to other cancer diagnoses in practice, which provides an option to a more interpretable, more accurate cancer diagnosis process.
References
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Data Mining: Concepts and Techniques
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Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Weka: Practical machine learning tools and techniques with Java implementations
TL;DR: The Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms.
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