Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

Diz, Joana; Marreiros, Goreti; Freitas, Alberto

doi:10.1007/s10916-016-0561-y

Cited by 60 publications

(44 citation statements)

References 35 publications

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“…In another study (17), Random Forest was demonstrated to have the best classification performance for the majority of the tested group, while in the case of masses texture, Naive Bayes had the best performance.…”

Section: Literature Reviewmentioning

confidence: 95%

Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer

Masoudiasl

Vahdat

Hessam

et al. 2019

Iran Red Crescent Med J

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Background: Breast cancer is the most common cancer in women, which has not been completely cured yet. The traditional approaches have low accuracy for breast cancer detection. However, intelligent techniques have been recently used in medical research to distinguish infected individuals from healthy ones, accurately. Objectives: In this study, we aim to develop an ensemble machine learning (ML) method to distinguish tumor samples from healthy samples robustly. Methods: We used an Imperial Competitive Algorithm coupled with a Fuzzy System (ICA-Fuzzy-SR) to identify the most influencing features to recognize tumor samples. To evaluate the proposed method, we used the publicly available Wisconsin Breast Cancer Dataset (WBCD). Results: Benchmarking with the current existing leading methods indicates that our proposed method achieves 95.45% prediction accuracy, which is 3% better than those reported in previous studies. Conclusions: Such results achieve while our model is significantly faster than previously proposed models to solve this problem.

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Section: Literature Reviewmentioning

confidence: 95%

Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer

Masoudiasl

Vahdat

Hessam

et al. 2019

Iran Red Crescent Med J

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“…The research paper done by Joana Diz Goreti Marreiros & Alberto Freitas [9] presents new computer based diagnosis system. By using this technique false positive diagnosis test can be reduced.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

Padmapriya

Vadivu²

2019

SSRN Journal

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The most common type of cancer in women worldwide is the Breast Cancer. Breast cancer may be detected early using Mammograms, probably before it's spread. Recurrent breast cancer could occur months or years after initial treatment. The cancer could return within the same place because the original cancer (local recurrence), or it may spread to different areas of your body (distant recurrence). Early stage treatment is done not only to cure breast cancer however additionally facilitate in preventing its repetition/recurrence. Data mining algorithms provide assistance in predicting the early-stage breast cancer that continually has been difficult analysis drawback. The projected analysis can establish the most effective algorithm that predicts the recurrence of the breast cancer and improve the accuracy the algorithms. Large information like Clump, Classification, Association Rules, Prediction and Neural Networks, Decision Trees can be analyzed using data mining applications and techniques.

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“…A decision support system with the employment of data mining approach which could assist oncologists to classify and diagnose breast cancer [31]. Two Portuguese-based binary class mammography datasets were used in this study and key features in the images were then selected using feature extraction.…”

Section: Other Breast Cancer Datasetsmentioning

confidence: 99%

Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer

Rajendran¹,

Jayabalan²,

Thiruchelvam³

et al. 2019

IJMLC

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Survivability of patients suffering from breast cancer varies according to the stages. The early detection of breast cancer increase the longevity of patients. However, the number of risk factors involved in the detection exponentially increases with the medical examinations. The need for automated data mining techniques to enable cost-effective and early prediction of cancer is rapidly becoming a trend in healthcare industry. The optimal techniques for prediction and diagnosis differs significantly due to the risk factors. This study reviews article provides a holistic view of the types of data mining techniques used in prediction of breast cancer. On a whole, the computer-aided automatic data mining techniques that are commonly employed in diagnosis and prognosis of chronic diseases include Decision Tree, Naï ve Bayes, Association rule, Multilayer Perceptron (MLP), Random Forest, and Support Vector Machines (SVM), among others. The accuracy and overall performance of the classifiers differ for every dataset and thereby this article attempts to provide a mean to understand the approaches involved in the early prediction.

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Applying Data Mining Techniques to Improve Breast Cancer Diagnosis

Cited by 60 publications

References 35 publications

Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer

Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer

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