RANSAC-based Enhancement in Drug Concentration Prediction Using Support Vector Machine

Training Support Vector Machines (SVMs) to predict drugs concentrations is often difficult because of the high level of noise in the training data, due to various kinds of measurement errors. We apply RANdom SAmple Consensus (RANSAC) algorithm in this paper to solve this problem, enhancing the prediction accuracy by more than 40% in our particular case study. A personalized sample selection method is proposed to further improve the prediction result in most cases.


Published in:
Proceedings of the International Workshop on Innovative Simulation for Healthcare (IWISH)
Presented at:
International Workshop on Innovative Simulation for Healthcare (IWISH), Vienna, Austria, September 19-21, 2012
Year:
2012
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 Record created 2012-10-02, last modified 2018-11-18

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