A novel redundant information elimination aided classification approach for cervical cancer diagnosis

Cervical cancer is one of the most dangerous diseases among female diseases. Because its clinical manifestation is not obvious, existing diagnostic approaches rely more on regular examination and timely detection of lesions. In this paper, the application of Support Vector Machine (SVM) classifier for classification and diagnosis is firstly introduced, Then a modified approach combining classifier model with partial least squares (PLS) regression is proposed, which can achieve further recognition and diagnosis after eliminating redundant information, so as to obtain a more stable and rapid diagnostic model for screening people at risk of disease. The improved approach and the principal component analysis-support vector machine (PCA-SVM) approach proposed by other researchers are applied to diagnose medical data. In this paper, the effectiveness of the two approaches is verified by simulation tests on cervical cancer data. Compared with existing approaches, results show that the proposed approach has better performance than PCA-SVM in the diagnosis of cervical cancer, and it achieves high classification accuracy by using fewer features.