Computational diagnosis and risk evaluation for canine lymphoma

• Published in: Computers in biology and medicine Vol. 53; pp. 279 - 290
• Main Authors: Mirkes, E.M, Alexandrakis, I, Slater, K, Tuli, R, Gorban, A.N
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-10-2014; Elsevier Limited
• Subjects: Advanced KNN; Age; Algorithms; Animals; Biomarkers; Cancer; Cancer diagnosis; Classification; Data analysis; Data Mining; Decision tree; Decision Trees; Diagnosis, Computer-Assisted - methods; Dogs; Female; Internal Medicine; Lymphoma; Lymphoma - diagnosis; Lymphoma - epidemiology; Lymphoma - veterinary; Male; Medical screening; Other; Proteins; Radial basis functions; Risk Assessment; Risk evaluation; Sensitivity and Specificity; Tumors; Vascular endothelial growth factor; Veterinary medicine; Advanced KNN; Data analysis; Radial basis functions; Risk evaluation; Classification; Cancer diagnosis; Decision tree
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2014.08.006

Abstract The canine lymphoma blood test detects the levels of two biomarkers, the acute phase proteins (C-Reactive Protein and Haptoglobin). This test can be used for diagnostics, for screening, and for remission monitoring as well. We analyze clinical data, test various machine learning methods and select the best approach to these oblems. Three families of methods, decision trees, kNN (including advanced and adaptive kNN) and probability density evaluation with radial basis functions, are used for classification and risk estimation. Several pre-processing approaches were implemented and compared. The best of them are used to create the diagnostic system. For the differential diagnosis the best solution gives the 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 provides the best result, with sensitivity and specificity of 81.4% and > 99 % , respectively (using the same input features). If the clinical symptoms (Lymphadenopathy) are considered as unknown then a decision tree with CRP and Hapt only provides sensitivity 69% and specificity 83.5%. The lymphoma risk evaluation problem is formulated and solved. The best models are selected as the system for computational lymphoma diagnosis and evaluation of the risk of lymphoma as well. These methods are implemented into a special web-accessed software and are applied to the problem of monitoring dogs with lymphoma after treatment. It detects recurrence of lymphoma up to two months prior to the appearance of clinical signs. The risk map visualization provides a friendly tool for exploratory data analysis.