Support vector machines combined with feature selection for breast cancer diagnosis

Akay, Mehmet Fatih

doi:10.1016/j.eswa.2008.01.009

Cited by 693 publications

(301 citation statements)

References 14 publications

Supporting

Mentioning

283

Contrasting

Unclassified

Order By: Relevance

“…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. An SVM-based algorithm combined with feature selection was proposed to achieve 99.51% when classifying the WBCD data [84]. This method chose the best feature combination (of five features) by calculating the F-score of different feature combinations.…”

Section: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

View full text Add to dashboard Cite

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.

show abstract

Section: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

View full text Add to dashboard Cite

show abstract

“…Unlike traditional SLT, which is based on structural risk minimization (SRM), SVM is a new universal learning theory based on Empirical Risk Minimization (ERM). Given its excellent learning and generalization ability, SVM has been widely employed in many fields, such as financial forecasting, image processing, face detection, and handwritten digit recognition [63][64][65][66][67].…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

2016

View full text Add to dashboard Cite

Abstract:Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries-Matusita (J-M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.

show abstract

“…Evaluation and decision making process from expert medical diagnosis is key important factor. However, intelligent classification algorithm may help doctor especially in minimizing error from unexperienced practitioners [3].…”

Section: Litreature Reviewmentioning

confidence: 99%

“…It is suggested to do tumor evaluation test every 4-6 weeks. Based on that reason, benign and malignant detection based on classification features become very important [3].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

Utomo¹,

Kardiana²,

Yuliwulandari³

2014

ijarai

View full text Add to dashboard Cite

Abstract-Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks (BP ANN) has some limitations. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. In this research, we implemented ANN with extreme learning techniques for diagnosing breast cancer based on Breast Cancer Wisconsin Dataset. Results showed that Extreme Learning Machine Neural Networks (ELM ANN) has better generalization classifier model than BP ANN. The development of this technique is promising as intelligent component in medical decision support systems.

show abstract

Support vector machines combined with feature selection for breast cancer diagnosis

Cited by 693 publications

References 14 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

Contact Info

Product

Resources

About