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NMF in Screening Some Spirometric Data, an Insight into 12-Dimensional Data Space

  • Anna M. Bartkowiak
  • Jerzy Liebhart
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10244)

Abstract

We present the usage of the Non-negative Matrix Factorization (NMF), an unsupervised machine learning method, which learns normal and abnormal state of patient’s ventilatory systems. This is done using samples of patients having defects of obturative and restrictive kind and a control group.

We show that the NMF method can identify patients being in the normal state and screen them off from the remaining patients; however the kind of the ventilatory disorder for the remaining patients is not recognized. This is confronted with clustering provided by the k-means method and visualization of the 12-dimensional data using heatmaps and Kohonen’s self-organizing maps.

The data set can be reconstructed with a 0.9746 accuracy (fraction of explained variance) from 6 base vectors provided by the NMF and using appropriate encoders provided also by the NMF; while 3 factors yield an 0.8573 fraction of explained variance.

Keywords

Healthy state Abnormal state Non-negative matrix factorization (NMF) Heatmap Self-organizing map (SOM) Inner factors Reconstruction of data 

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Institute of Computer ScienceWroclaw UniversityWroclawPoland
  2. 2.Department and Clinic of Internal Diseases and AllergologyWroclaw Medical UniversityWroclawPoland

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