Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/32072
Title: Computational diagnosis of canine lymphoma
Authors: Mirkes, E. M.
Alexandrakis, I.
Slater, K.
Tuli, R.
Gorban, A. N.
First Published: 1-Jan-2014
Presented at: 2nd International Conference on Mathematical Modeling in Physical Sciences 2013 (IC-MSQUARE 2013)
Publisher: IOP PUBLISHING LTD
Citation: Journal of Physics: Conference Series 490 (2014) 012135
Abstract: One out of four dogs will develop cancer in their lifetime and 20% of those will be lymphoma cases. PetScreen developed a lymphoma blood test using serum samples collected from several veterinary practices. The samples were fractionated and analysed by mass spectrometry. Two protein peaks, with the highest diagnostic power, were selected and further identified as acute phase proteins, C-Reactive Protein and Haptoglobin. Data mining methods were then applied to the collected data for the development of an online computer-assisted veterinary diagnostic tool. The generated software can be used as a diagnostic, monitoring and screening tool. Initially, the diagnosis of lymphoma was formulated as a classification problem and then later refined as a lymphoma risk estimation. Three methods, decision trees, kNN and probability density evaluation, were used for classification and risk estimation and several preprocessing approaches were implemented to create the diagnostic system. For the differential diagnosis the best solution gave a sensitivity and specificity of 83.5% and 77%, respectively (using three input features, CRP, Haptoglobin and standard clinical symptom). For the screening task, the decision tree method provided the best result, with sensitivity and specificity of 81.4% and >99%, respectively (using the same input features). Furthermore, the development and application of new techniques for the generation of risk maps allowed their user-friendly visualization.
DOI Link: 10.1088/1742-6596/490/1/012135
ISSN: 1742-6588
eISSN: 1742-6596
Links: http://iopscience.iop.org/1742-6596/490/1/012135/
http://hdl.handle.net/2381/32072
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (CC BY 3.0) (http://creativecommons.org/licenses/by/3.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Appears in Collections:Published Articles, Dept. of Mathematics

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