Medical Diagnosis Based on Nonlinear Manifold Discriminative Projection
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.
KeywordsMedical diagnosis Machine learning Classification Discriminant projection Unlabeled test data
This research was supported in part by the Chinese National Natural Science Foundation with Grant nos. 61402395, 61472343 and 61379066, Natural Science Foundation of Jiangsu Province under contracts BK20140492 and BK20151314, Jiangsu government scholarship funding, Jiangsu overseas research and training program for university prominent young and middle-aged teachers and presidents.
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