Comput. Biol. Med. - SVM-based feature selection to optimize sensitivity-specificity balance applied to weaning.

Tópicos

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Resumo

Classification algorithms with unbalanced datasets tend to produce high predictive accuracy over the majority class, but poor predictive accuracy over the minority class. This problem is very common in biomedical data mining. This paper introduces a Support Vector Machine (SVM)-based optimized feature selection method, to select the most relevant features and maintain an accurate and well-balanced sensitivity-specificity result between unbalanced groups. A new metric called the balance index (B) is defined to implement this optimization. The balance index measures the difference between the misclassified data within each class. The proposed optimized feature selection is applied to the classification of patients' weaning trials from mechanical ventilation: patients with successful trials who were able to maintain spontaneous breathing after 48 h and patients who failed to maintain spontaneous breathing and were reconnected to mechanical ventilation after 30 min. Patients are characterized through cardiac and respiratory signals, applying joint symbolic dynamic (JSD) analysis to cardiac interbeat and breath durations. First, the most suitable parameters (C+,C-,s) are selected to define the appropriate SVM. Then, the feature selection process is carried out with this SVM, to maintain B lower than 40%. The best result is obtained using 6 features with an accuracy of 80%, a B of 18.64%, a sensitivity of 74.36% and a specificity of 82.42%.

Resumo Limpo

classif algorithm unbalanc dataset tend produc high predict accuraci major class poor predict accuraci minor class problem common biomed data mine paper introduc support vector machin svmbase optim featur select method select relev featur maintain accur wellbalanc sensitivityspecif result unbalanc group new metric call balanc index b defin implement optim balanc index measur differ misclassifi data within class propos optim featur select appli classif patient wean trial mechan ventil patient success trial abl maintain spontan breath h patient fail maintain spontan breath reconnect mechan ventil min patient character cardiac respiratori signal appli joint symbol dynam jsd analysi cardiac interbeat breath durat first suitabl paramet ccs select defin appropri svm featur select process carri svm maintain b lower best result obtain use featur accuraci b sensit specif

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