Abstract
Clinical data analysis is of fundamental importance, as classifications and detailed characterizations of diseases help physicians decide suitable management for patients, individually. In our study, we adopt diffusion maps to embed the data into corresponding lower dimensional representation, which integrate the information of potentially nonlinear progressions of the diseases. To deal with nonuniformaity of the data, we also consider an alternative distance measure based on the estimated local density. Performance of this modification is assessed using artificially generated data. Another clinical dataset that comprises metabolite concentrations measured with magnetic resonance spectroscopy was also classified. The algorithm shows improved results compared with conventional Euclidean distance measure.
Highlights
Exploring the behavior and patterns of clinical data is crucial, and is mostly done with statistics or linear analysis
The SCA dataset consists of three different groups, namely the 63 spinocerebellar ataxia type 3 (SCA3) patients, 98 multiple system atrophy (MSA) patients, and 44 normal subjects
Based on the clustering properties of the diffusion maps, we analyze the clinical data in a lower dimensional space induced by distance measure of the diffusion maps
Summary
Exploring the behavior and patterns of clinical data is crucial, and is mostly done with statistics or linear analysis. Several factors may make these approaches incapable of dealing the data effectively and efficiently. Sometimes the higher dimensional data may lie on a lower dimensional space. Classical statistical methods and linear analysis may not provide insightful information on such kind of data. A new approach, namely diffusion maps [1], had been proposed to deal with high dimensional data.
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