Prediction model building and feature selection with support vector machines in breast cancer diagnosis

Huang, Cheng-Lung; Liao, Hung‐Chang; Chen, Mu-Chen

doi:10.1016/j.eswa.2006.09.041

Cited by 126 publications

(60 citation statements)

References 43 publications

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“…Apart from SVM-based model, another type of diagnosis model called LDA (Linear discriminate analysis) was also constructed in this research. After comparing the accuracies of both SVM and LDA, the accuracy of SVM was far better than that of LDA [59].…”

Section: Classification Techniques Examples In Healthcarementioning

confidence: 99%

Techniques of Data Mining In Healthcare: A Review

Ahmad¹,

Qamar²,

Rizvi³

2015

IJCA

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Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

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Section: Classification Techniques Examples In Healthcarementioning

confidence: 99%

Techniques of Data Mining In Healthcare: A Review

Ahmad¹,

Qamar²,

Rizvi³

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…Sequential backward and forward search to choose the most important mixture of features using the multilayer perceptron neural network to classify tumours had been proposed by Nezafat et.al [5]. F-score for finding the DNA virus discrimination was proposed for the selection of the best subset of DNA diseases for breast tumour analysis based on SVM [6] and [7]. Combining an SVM-based approach with feature reduction technique for diagnosis of breast tumour was proposed by Akay [8].…”

Section: Related Workmentioning

confidence: 99%

“…Combining an SVM-based approach with feature reduction technique for diagnosis of breast tumour was proposed by Akay [8]. Through the use of the F-score for the measuring of the feature discrimination, the study conducted a consuming time for the optimal parameters adjusting mixed on the precision diagnosis to nominate the best subclass of the basic tumour attributes for learning stage by support vector machine [7]. Prasad, Biswas, and Jain [9] proposed a another combination method of SVM and heuristics to find out the significant attributes subclass for SVM learning stage rather than the extensive search.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Breast Cancer Diagnosis Scheme based on Two-Step-SVM Technique

Osman¹

2017

ijacsa

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Abstract-This paper proposes an automatic diagnostic method for breast tumour disease using hybrid Support Vector Machine (SVM) and the Two-Step Clustering Technique. The hybrid technique is aimed at improving the diagnostic accuracy and reducing diagnostic miss-classification, thereby solving the classification problems related to Breast Tumour. To distinguish the hidden patterns of the malignant and benign tumours, the Two-Step algorithm and SVM have been combined and employed to differentiate the incoming tumours. The developed hybrid method enhances the accuracy by 99.1% when examined on the UCI-WBC data set. Moreover, in terms of evaluation measures, it has been shown experimentally results that the hybrid method outperforms the modern classification techniques for breast cancer diagnosis.

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“…Logistic regression for the estimation of relative risk for various medical conditions such as Diabetes, Angina, stroke etc [59].…”

Section: Regression Illution In Healthcarementioning

confidence: 99%

Methodologies of Data Mining in Healthcare: A Inspection

Periasamy¹,

Mohan²

2017

IJARCSSE

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Abstract-Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

show abstract

Prediction model building and feature selection with support vector machines in breast cancer diagnosis

Cited by 126 publications

References 43 publications

Techniques of Data Mining In Healthcare: A Review

Techniques of Data Mining In Healthcare: A Review

An Enhanced Breast Cancer Diagnosis Scheme based on Two-Step-SVM Technique

Methodologies of Data Mining in Healthcare: A Inspection

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