Analysis of Oral Carcinogenesis Factors Based on Non Negative Matrix Evolutionary Spectrum Clustering Decomposition

Abstract

To explore the risk factors of Oral Submucosal fibrous carcinogenesis and provide theoretical basis for selfprevention and clinical treatment. First of all, an evolutionary non negative matrix factorization framework based on clustering quality is proposed for dynamic oral carcinogenesis detection. Then, the equivalence among evolutionary spectral clustering, ENMF and density optimization of evolutionary modules is proved theoretically. At the same time, based on this equivalence, a new semi-supervised ENMF algorithm is proposed by adding a priori information to ENMF without increasing the time complexity. Using this algorithm, 42 patients with Oral Submucosal Fibrosis and 40 non-cancerous patients with oral submucosal fibrosis were analyzed by single factor and multi-factor analysis to study the quantitative relationship between Oral Submucosal Fibrosis and 14 related factors. The results suggested that age, betel nut chewing duration, smoking duration and leukoplakia or lichen planus were the four factors which were introduced into the cluster quality analysis. Age, betel nut chewing duration, smoking duration and complications of leukoplakia or lichen planus are risk factors for Oral Submucosal fibrous carcinogenesis.