An expert system for detection of breast cancer based on association rules and neural network

Karabatak, Murat; İnce, Melih Cevdet

doi:10.1016/j.eswa.2008.02.064

Cited by 424 publications

(180 citation statements)

References 15 publications

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“…The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. In 2009, an expert system was introduced to detect BC in WBCD based on association rules (AR) and ANN [70]. The AR approach has been used to reduce the dimension of the database from 9 attributes to 4 attributes, without reducing the accuracy, while combining it with a basic ANN method.…”

Section: Annsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

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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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Section: Annsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

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“…The extracted features are input to the neural classifier. The neural network used here is a three layer network with 'n' unit in the input layer, one unit in the hidden layer and output layer (Karabatak and Ince, 2009;Ren et al, 2011). The desired output from the neural network is whether the mammogram is normal or abnormal.…”

Section: Pixel Intensity Mean Featuresmentioning

confidence: 99%

Mammogram Analysis Based on Pixel Intensity Mean Features

Mohd¹

2012

Journal of Computer Science

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Problem statement:In the recent years, Computer Aided Diagnosis (CAD) can be very useful for detection of breast cancer. Mammography can be used as an efficient tool for breast cancer diagnosis. A computer based diagnosis and classification system can reduce unnecessary biopsy. Approach: This study investigates a new approach to the classification of mammogram images based on pixel intensity mean features. The proposed method for the classification of normal and abnormal (cancerous) pattern is a two step process. The first step is feature extraction. The intensity based features are extracted from the digital mammograms. The second step is the classification process, differentiating between normal and abnormal pattern. Artificial neural networks are used to classify the data. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM), benchmark database. Results and Conclusion: Experiments are performed to verify that the proposed pixel intensity mean features improve the accuracy of the classification. The proposed CAD system achieves better classification performance with the accuracy of 98%.

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“…In the literature, there exist lots of simple & hybrid classifiers used on Wisconsin Breast Cancer (WBC) and Pima Indian Diabetes (PID) datasets. Karabatak and Ince [5] used Association Rules (AR) with NN structure on WBC. Wang [6] examined different classifiers using WEKA for pattern classification process on WBC.…”

Section: Introductionmentioning

confidence: 99%

RF Ensemble Novelties Based on Optimized & Backpropagated NNs

Koyuncu¹,

Ceylan²

2017

IJMLC

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Abstract-This paper presents a classifier model based on Rotation Forest (RF) ensemble structure for biomedical data classification. Classifiers based on RF are generally implemented by using Decision Trees. In this study, optimized Neural Network (NN) is preferred as being the base classifier in RF so as to achieve higher classification performance. Two optimization techniques, Artificial Bee Colony Optimization (ABC) and Particle Swarm Optimization (PSO), are utilized to improve the performance of NN for escaping from local minima. In this way, PSO-NN and ABC-NN based RF structures are designed, and they are called as RF (PSO-NN) and RF (ABC-NN), respectively. In these classifiers, initial weights of NNs are found by using PSO or ABC algorithms. The implemented classifiers based on RF are applied to biomedical datasets (Wisconsin Breast Cancer and Pima Indian Diabetes) that are taken from UCI Machine Learning Repository. Furthermore, fourteen different ensemble structures are generated using these algorithms to prove the superiority of the proposed method. When the results are examined using several performance metrics, it is seen that RF (ABC-NN) classifier achieves to more reliable and better results than other classifiers.Index Terms-Rotation forest, particle swarm optimization, artificial bee colony optimization, neural networks, biomedical data classification.

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An expert system for detection of breast cancer based on association rules and neural network

Cited by 424 publications

References 15 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Mammogram Analysis Based on Pixel Intensity Mean Features

RF Ensemble Novelties Based on Optimized & Backpropagated NNs

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