Predicting breast cancer recurrence using effective classification and feature selection technique

Pritom, Ahmed Iqbal; Munshi, Md. Ahadur Rahman; Sabab, Shahed Anzarus; Shihab, Shihabuzzaman

doi:10.1109/iccitechn.2016.7860215

Cited by 62 publications

(24 citation statements)

References 5 publications

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“…In [8], the study was to investigate the probability of breast cancer as well as the probability of recurrent breast cancer using various data mining techniques. Cancer patient data was collected from Wisconsin dataset of the UCI machine learning.…”

Section: Review Of Previous Methodsmentioning

confidence: 99%

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019

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Breast cancer is the second leading cancer among women worldwide, the incidence of which has been reported to increases every year due to factors such as inheritance, lifestyle, and dietary habits [1]. Industrialization and urbanization are among the other contributing factors to the increase incidence of breast cancer. On the other hand, new facilities have been used for the early diagnosis of this chronic disease. The prevalence rate of breast cancer has been reported to be higher in high-income countries, while the incidence rate is on the rise in low-income countries as well, such as the regions in Africa, Asia, and Latin America [2]. In India, the mean age range of the high-risk population for breast cancer has been estimated at 43-46 years, and women aged 53-57 years are reported to be more susceptible to breast cancer [3].

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Section: Review Of Previous Methodsmentioning

confidence: 99%

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019

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“…AUC depicts the ROC of a classifier. The larger the estimated value of AUC, the more feasible the display of the classifier [28].…”

Section: Correlation With Target Variablementioning

confidence: 99%

Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Aggrawal

Pal

2020

SN COMPUT. SCI.

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Due to the accessibility of data with multiple features, many feature determination techniques available in written form. These features promote data with extremely high measurement values. The feature determination strategy provides us with a way to reduce calculation time, improve prediction execution, and have a better understanding of data in machine learning, as well as a way to recognize applications. As pointed out by related works that have been reviewed, in general, existing works only focus on amplifying classification accuracy. For real-world applications, the selected subset of features must be continuous instead. In this research, proposes a sequential feature selection algorithm for detecting death events in heart disease patients during treatment to select the most important features. Several machine learning algorithms (LDA, RF, GBC, DT, SVM, and KNN) are used. In addition, the accuracy obtained by this method (SFS) is compared with the accuracy of the classifier. The confusion matrix, ROC curve, precision, recall rate, and f1-score are also calculated to verify the results obtained by the SFS algorithm. The experimental results show that for Random Forest Classifier_FS, the SFS method reaches 86.67% accuracy.

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“…A. I. Pritom [8] applied Naive Bayes, C4.5 Decision Tree and Support Vector Machine (SVM) classification algorithms to calculate the prediction accuracy of breast cancer recurrence. Data were collected from Wisconsin dataset of UCI machine learning Repository with total 35 attributes.…”

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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Predicting breast cancer recurrence using effective classification and feature selection technique

Cited by 62 publications

References 5 publications

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

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

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