A Machine Learning Approach for the Classification of Disease Risks in Time Series

Mediterranean Conference on Embedded Computing (MECO), pp. 1–5

Abstract

In this work, a new hybrid algorithm for disease risk classification is proposed. The proposed methodology is based on Dynamic Time Warping (DTW). This methodology can be applied to time series from various domains such as vital sign time series available in medical big data. To validate our methodology, we applied it to risk classification for sepsis, which is one of the most challenging problems within the area of medical data analysis. In the first step the algorithm uses different statistical properties of time series features. Furthermore, using differently labeled training data sets, we created a DTW Barycenter Averaging (DBA) on each feature. In the second step, validation data sets and DTW are used to validate the precision of classification and the final results are compared. The performance of our methodology is validated with real medical data and on six different criteria definitions for the sepsis diseases. Results show that our algorithm performed, in the best case, with precision and recall of 96,38% and 90,90%, respectively.

Authors 10

1. Lejla Begic Fazlic

   Trier University of Applied Sciences

   Affiliation as printed

   ISS, Trier University of Applied Sciences, Trier, Germany

2. Ahmed Hallawa Aachen ISEK Research and Teaching Area

   RWTH Aachen University

   Affiliation as printed

   ISEK Research and Teaching Area, RWTH Aachen University, Aachen, Germany

3. Matthias Dziubany

   Trier University of Applied Sciences

   Affiliation as printed

   ISS, Trier University of Applied Sciences, Trier, Germany

4. Marlies Morgen

   Trier University of Applied Sciences

   Affiliation as printed

   ISS, Trier University of Applied Sciences, Trier, Germany

5. Marvin Schacht

   Trier University of Applied Sciences

   Affiliation as printed

   ISS, Trier University of Applied Sciences, Trier, Germany

6. Anke Schmeink Aachen ISEK Research and Teaching Area

   RWTH Aachen University

   Affiliation as printed

   ISEK Research and Teaching Area, RWTH Aachen University, Aachen, Germany

7. Lukas Märtin Aachen Department of Intensive and Intermediate Care

   Universitätsklinikum Aachen

   Affiliation as printed

   Department of Intensive and Intermediate Care, University Hospital Aachen, Aachen, Germany

8. Arne Peine Aachen Department of Intensive and Intermediate Care

   Universitätsklinikum Aachen

   Affiliation as printed

   Department of Intensive and Intermediate Care, University Hospital Aachen, Aachen, Germany

9. Thomas R. Vollmer
   Affiliation as printed

   Philips GmbH Innovative Technologies, Aachen, Germany

10. Guido Dartmann

    Trier University of Applied Sciences

    Affiliation as printed

    ISS, Trier University of Applied Sciences, Trier, Germany