Prediction of periventricular leukomalacia occurrence in neonates using a novel unsupervised learning method

Dieter Bender, Ali Jalali, C. Nataraj

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Prior work has documented that Supp ort Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). A preceding study indicated that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This preliminary work, as well as the complex nature of the PVL data suggested integration of the active learning algorithm into the overall SVM framework. The present study supports this initial hypothesis and shows that active learning SVM type classifier performs considerably well and outperforms normal SVM type classifiers when dealing with clinical data of high dimensionality.

Original languageEnglish (US)
Title of host publicationDynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791846193
DOIs
StatePublished - 2014
Externally publishedYes
EventASME 2014 Dynamic Systems and Control Conference, DSCC 2014 - San Antonio, United States
Duration: Oct 22 2014Oct 24 2014

Publication series

NameASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Volume2

Conference

ConferenceASME 2014 Dynamic Systems and Control Conference, DSCC 2014
CountryUnited States
CitySan Antonio
Period10/22/1410/24/14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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