Breast cancer diagnosis using least square support vector machine

Polat, Kemal; Güneş, Salih

doi:10.1016/j.dsp.2006.10.008

Cited by 341 publications

(137 citation statements)

References 10 publications

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“…The result has proved the basic SVM approach achieved a high accuracy given quality data in BC. Then in 2007, a least square SVM (LS-SVM) [82] was used to improve the accuracy up to 98.53% [83]. The main difference between SVM and LS-SVM is that LS-SVM uses a set of linear equations instead of quadratic programming in SVM due to the equality constraints in the formulation.…”

Section: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

202

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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: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

202

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“…Artificial Neural networks [14,16] and support vector machine [12,17,13] have been utilized as a classification task in medical diagnosis. Lo et al [14] utilized artificial neural network (ANN) approach to develop computer-aided diagnosis of mammography using an optimally minimized number of input features.…”

Section: Introductionmentioning

confidence: 99%

“…The result showed that he ANN with the four optimized features was significantly better than expert radiologists. Polat et al [17] conducted breast cancer diagnosis using least square support vector machine (LS-SVM) classifier algorithm. The obtained classification accuracy was 98.53%, utilizing LS-SVM, the obtained results show that the machine learning method can be effective in diagnosing breast cancer and point on a direction of designing a new intelligent assistance diagnosis systems using SVM.…”

Section: Introductionmentioning

confidence: 99%

Predicting Cancer-Related Proteins in Protein-Protein Interaction Networks using Network Approach and SMO-SVM Algorithm

Enyinnaya¹

2015

IJCA

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An early diagnosis of cancer is crucial to improving the survival rate and to prolong the lives of patients. With the large amounts of medical data available in the medical field, applying data mining tools and an efficient prediction methodology to diagnose diseases can lead to useful knowledge to support medical professionals in saving lives. This paper explores genomic interactions networks, investigating protein-protein interaction networks to predict cancer related proteins using sequential minimal Optimization (SMO) for training Support Vector Machine (SVM). The WEKA software was utilized as the data mining tool, which is an open source collection of machine learning algorithms. The provided data set was studied and analyzed in order to build a useful and reliable model to predict cancer and non-cancer related proteins.

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“…Sahan et al [11] achieved a success rate of 99.14% using a hybrid model based on fuzzy artificial immunity and K-nearest neighbor in their studies. Polat and Güneş [12] achieved a success rate of 98.53% by least squares support vector machine (LS-SVM) in their studies. Karabatak and Ince [13] demonstrated a success rate of 97.4% by developing a model based on association rules and an ANN.…”

Section: Studies For the Diagnosis Of Bcmentioning

confidence: 99%

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

Kaya¹

2013

Turk J Elec Eng & Comp Sci

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Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing the proposed hybrid model and the success rate of the RS + ELM model was determined as 100%. Moreover, the most appropriate attributes for the diagnosis of breast cancer were determined from the WBCD in this study. It is considered that the proposed method will be useful in similar medical practices.

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Breast cancer diagnosis using least square support vector machine

Cited by 341 publications

References 10 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Predicting Cancer-Related Proteins in Protein-Protein Interaction Networks using Network Approach and SMO-SVM Algorithm

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

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