Neural Network Based Tumor Marker Prediction

Witold Jacak, Karin Pröll

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

In this paper a system for the prediction of tumor marker values based on standard blood is presented. Several neural networks are used to learn from blood examination measurements and predict tumor markers in case these values are missing. In a post processing step the predicted values are evaluated in a fuzzy logic like style against different hypotheses and the best hypothesis is used to optimize the predicted values and its plausibility. These predicted values can then be used as input for a second system to support decision making in cancer diagnosis. A variety of experiments with tumor marker C153 show that we can get a prediction accuracy of more than 90%. Our experiments are based on hundreds of samples of up to 27 different features (blood parameters) per vector. We try to predict distinct values, classes of values and a combination of classes and values for specific marker types.
Original languageEnglish
Title of host publicationProceedeings of 5th International Conference on Broadband Communication, Information Technology & Biomedical Applications, BroadCom 2010
PublisherRiver Publishers (Series in Information Science)
Pages1-6
Publication statusPublished - 2010
EventBroadCom 2010 - 5th International Conference on Broadband Communication, Information Technology & Biomedical Applications - Malaga, Spain
Duration: 15 Dec 201019 Dec 2010

Conference

ConferenceBroadCom 2010 - 5th International Conference on Broadband Communication, Information Technology & Biomedical Applications
CountrySpain
CityMalaga
Period15.12.201019.12.2010

Keywords

  • neural network
  • tumor marker prediction
  • decision support system

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