An application of machine learning to haematological diagnosis

[1]  Roland Eils,et al.  Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach , 2017, Scientific Reports.

[2]  Bram van Ginneken,et al.  Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning , 2017, Radiological Physics and Technology.

[3]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[4]  Marleen de Bruijne,et al.  Machine learning approaches in medical image analysis: From detection to diagnosis , 2016, Medical Image Anal..

[5]  Edmund E Wilkes,et al.  Using machine learning to predict laboratory test results , 2016, Annals of clinical biochemistry.

[6]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[7]  Brian R. Jackson,et al.  Primary Care Physicians' Challenges in Ordering Clinical Laboratory Tests and Interpreting Results , 2014, The Journal of the American Board of Family Medicine.

[8]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[9]  Anne-Laure Boulesteix,et al.  Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics , 2012, WIREs Data Mining Knowl. Discov..

[10]  Aaron Trefler,et al.  The Future of Medical Diagnostics: Large Digitized Databases , 2012, The Yale journal of biology and medicine.

[11]  Armağan Kanca,et al.  Evaluation and Comparison of Diagnostic Test Performance Based on Information Theory , 2012 .

[12]  Matjaz Kukar,et al.  Image processing and machine learning for fully automated probabilistic evaluation of medical images , 2011, Comput. Methods Programs Biomed..

[13]  Igor Kononenko,et al.  Modern parameterization and explanation techniques in diagnostic decision support system: A case study in diagnostics of coronary artery disease , 2011, Artif. Intell. Medicine.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Akin Özçift,et al.  Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis , 2011, Comput. Biol. Medicine.

[16]  E. Carson.  Computer Methods and Programs in Biomedicine , 2010 .

[17]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[18]  Gilles R. Ducharme,et al.  Computational Statistics and Data Analysis a Similarity Measure to Assess the Stability of Classification Trees , 2022 .

[19]  Fan Yang,et al.  Using random forest for reliable classification and cost-sensitive learning for medical diagnosis , 2009, BMC Bioinformatics.

[20]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[21]  S. van Buuren.  Multiple imputation of discrete and continuous data by fully conditional specification , 2007, Statistical methods in medical research.

[22]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[23]  Derek Greene,et al.  Ensemble clustering in medical diagnostics , 2004, Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems.

[24]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[25]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[26]  L Thomas,et al.  Evidence-based laboratory medicine. , 2001, Clinical laboratory.

[27]  Thomas G. Dietterich.  Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[28]  OpitzDavid,et al.  Popular ensemble methods , 1999 .

[29]  W. Benish.  Relative Entropy as a Measure of Diagnostic Information , 1999, Medical decision making : an international journal of the Society for Medical Decision Making.

[30]  Ron Kohavi,et al.  The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.

[31]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[32]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[33]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[34]  M. C. Peterson,et al.  Contributions of the history, physical examination, and laboratory investigation in making medical diagnoses. , 1992, The Western journal of medicine.