Medical Diagnosis Based on Nonlinear Manifold Discriminative Projection

Abstract

In recent years, medical diagnosis based on machine learning has become popular in the interdiscipline research of computer science and medical science. It is closely related with classification, which is one of the important problems in machine learning. However, the traditional classification algorithms can hardly appropriately solve high-dimensional medical datasets. Manifold learning as nonlinear dimensionality reduction algorithm can efficiently process high dimensional medical datasets. In this paper, we propose an algorithm based on Nonlinear Manifold Discriminative Projection (NMDP). Our algorithm incorporates the label information of medical data into the unsupervised LLE method, so that the transformed manifold becomes more discriminative. Then we apply the discriminant mapping to the unlabeled test data for classification. Experimental results show that our method exhibits promising classification performance on different medical data sets.