A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Kaya, Yılmaz

doi:10.3906/elk-1203-119

Cited by 23 publications

(11 citation statements)

References 25 publications

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“…output, the mathematical model with M hidden neurons can be defined as in Kaya (2013): In a network of the N training samples, the aim is with zero error:…”

Section: Extreme Learning Machine (Elm) Algorithmmentioning

confidence: 99%

“…With the ELM, the weightings belonging to neurons at the input layer, and the bias values belonging to neurons in the hidden and input layers, are all randomly generated. By contrast, the outputs from the hidden layer are computed analytically (Huang et al, 2006;Kaya, 2013). The most significant feature of the ELM model is that the learning process is very efficient.…”

Section: Introductionmentioning

confidence: 99%

“…It can learn thousands of times faster than conventional learning algorithms for feed-forward neural networks. The learning speed of other feed-forward neural networks is typically relatively slow, largely due to the slow gradient-based learning algorithms used in the training procedure (Kaya, 2013). Time, particularly, is a vital factor in diagnosis in medical applications.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Intelligent Diagnosis System for Thyroid Diseases Based on Extreme Learning Machine

Kaya¹

2015

AUBTD-A

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With iodine taken from outside, the thyroid gland is an organ that secretes hormones called thyroxin. All metabolic functions of human beings are controlled by these hormones. An overactive thyroid gland which is producing an excessive amount of these hormones causes hyperthyroidism, while an underactive thyroid gland that is not producing enough of these hormones causes hypothyroidism. The diagnosis of thyroid gland disorders by assessing the data of thyroid in clinical applications comes out as an important classification problem. In this study, Extreme Learning Machine (ELM) was applied to the thyroid data set taken from UCI machine learning repository. The ELM is single hidden layer feed-forward artificial neural network model which can be learnt fast. It was seen that the ELM, for the data set, has the upper hand in terms of both classification accuracy and speed when compared to other machine learning methods. The classification accuracy obtained through the ELM is 96.79% for 70-30% training-test partition.

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“…output, the mathematical model with M hidden neurons can be defined as in Kaya (2013): In a network of the N training samples, the aim is with zero error:…”

Section: Extreme Learning Machine (Elm) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast Intelligent Diagnosis System for Thyroid Diseases Based on Extreme Learning Machine

Kaya¹

2015

AUBTD-A

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“…The best reported accuracy was 99.74% with 10-fold cross validation [15]. ANN was implemented in other studies [18][19][20][21][22]. The best accuracy was 100%, with 80% of the data used for learning and the remaining 20% for testing [20].…”

Section: Introductionmentioning

confidence: 99%

Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses

Al-Rawi

2020

eijs

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Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.

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“…Maglogiannis et al [12] presented using support vector machine (SVM) for diagnosing the breast cancer both on Wisconsin Diagnostic Breast Cancer and the Wisconsin Prognostic Breast Cancer datasets. Kaya et al [13] proposed a novel approach based on rough set and extreme learning machine for distinguishing the benign or malignant breast cancer. Akay et al [14] proposed a novel SVM combined with feature selection for breast cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features

et al. 2019

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Background It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. Results In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution. Conclusions The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.

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A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Cited by 23 publications

References 25 publications

A Fast Intelligent Diagnosis System for Thyroid Diseases Based on Extreme Learning Machine

A Fast Intelligent Diagnosis System for Thyroid Diseases Based on Extreme Learning Machine

Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses

A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features

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