Decision Tree Classifiers for Classification of Breast Cancer

Hamsagayathri, P.; Sampath, P.

doi:10.22159/ijcpr.2017v9i1.17377

Cited by 13 publications

(4 citation statements)

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“…The researchers proposed a model with an accuracy of 83.5% and a ROC of 0.907. In [39], the breast cancer prediction was studied using various classification algorithms such as RepTree, J48, and random forest. Dataset used in this study was extracted from SEER repository with 762,691 samples and 134 features.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the dataset was obtained specific registry dataset format. Kaushik et al [38] used naïve Bayesian, RBF network, and decision tree techniques to predict breast cancer on Wisconsin dataset provided by the University of California Irvine machine learning repository [39]. They achieved the best accuracy of 97.36% related to the naïve Bayesian classifier.…”

Section: Discussionmentioning

confidence: 99%

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Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data

Sohrabei

Atashi

2021

Front Health Inform

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Introduction: Early detection breast cancer Causes it most curable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. Data mining techniques have a growing reputation in the medical field because of high predictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer.Material and Methods: The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using 20 fields (risk factor) of the database because database feature was restrictions. Used 10-fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators.Results: Naïve Bayesian and artificial neural network are better models for the prediction of breast cancer risks. Naïve Bayesian had accuracy of 93%, specificity of 93.32%, sensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, specificity of 91.98%, sensitivity of 92.69%, and ROC of 0.8.Conclusion: Strangely the different artificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy risk considers. The significant prognostic components affecting risk pace of bosom disease distinguished in this investigation, which were approved by risk, are helpful and could be converted into choice help devices in the clinical area.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data

Sohrabei

Atashi

2021

Front Health Inform

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“…In [1], the breast cancer diagnosis was studied using various classification algorithms such as Rep Tree, J48, and Random Forest. The dataset used in this study was extracted from SEER repository with 762,691 samples and 134 features.…”

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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“…The J48 classifier is the extension of decision tree ID3 and straightforward C4.5 algorithms with additional features like accounting for missing values, continuous attribute value, and derivation of rules [18,19]. This classifier utilizes top-down and greedy search through all possible branches to construct a decision tree [19].…”

Section: Classification Algorithmsmentioning

confidence: 99%

A Meta-Ensemble Classifier Approach: Random Rotation Forest

Taşçı

2019

Balkan Journal of Electrical and Computer Engineering

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Ensemble learning is a popular and intensively studied field in machine learning and pattern recognition to increase the performance of the classification. Random forest is very important for giving fast and effective results. On the other hand, Rotation Forest can get better performance than Random Forest. In this study, we present a meta-ensemble classifier, called Random Rotation Forest to utilize and combine the advantages of two classifiers (e.g. Rotation Forest and Random Forest). In the experimental studies, we use three base learners (namely, J48, REPTree, and Random Forest) and two metalearners (namely, Bagging and Rotation Forest) for ensemble classification on five datasets in UCI Machine Learning Repository. The experimental results indicate that Random Rotation Forest gives promising results according to base learners and bagging ensemble approaches in terms of accuracy rates, AUC, precision, recall, and F-measure values. Our method can be used for image/pattern recognition and machine learning problems.

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Decision Tree Classifiers for Classification of Breast Cancer

Cited by 13 publications

References 1 publication

Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data

Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data

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

A Meta-Ensemble Classifier Approach: Random Rotation Forest

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