title: HClass: Automatic classification tool for health pathologies using artificial intelligence techniques.
abstract: The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.
authors.affiliation: DeustoTech-LIFE, University of Deusto, Avda Universidades, 24, 48007, Bilbao, Spain.
authors.forename: Yolanda
authors.initials: Y
authors.lastname: Garcia-Chimeno
authors.affiliation: DeustoTech-LIFE, University of Deusto, Avda Universidades, 24, 48007, Bilbao, Spain.
authors.forename: Begonya
authors.initials: B
authors.lastname: Garcia-Zapirain
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
keywords: Classification; PCA; committee; cross-validation; machine learning