Hypertension Type Classification Using Hierarchical Ensemble of One-Class Classifiers for Imbalanced Data

The paper presents the research on the computer support system which is able to recognize the type of hypertension. This diagnostic problem is highly imbalanced, because only ca. 5% of patient suffering from hypertension are diagnosed as secondary hypertension. Additionally the secondary hypertension could be caused by several disorders (in our work we recognize the five most popular reasons) which require strikingly different therapies. Thus, appropriate classification methods, which take into consideration the nature of the decision task should be applied to this problem. We decided to employ the original classification methods developed by our team which have their origin in one-class classification and the ensemble learning. They quality was confirmed in our previous works. The accuracy of the chosen classifiers was evaluated on the basis of the computer experiments which were carried out on the real data set obtained from the hypertension clinic. The results of the experimental investigations confirmed usefulness of the proposed, hierarchical one-class classifier ensemble and could be applied in the real medical decision support systems.

[1]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[2]  Michal Wozniak,et al.  Soft computing methods applied to combination of one-class classifiers , 2012, Neurocomputing.

[3]  Jay Liebowitz,et al.  The Handbook of Applied Expert Systems , 1997 .

[4]  Jerzy Stefanowski,et al.  Identification of Different Types of Minority Class Examples in Imbalanced Data , 2012, HAIS.

[5]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[6]  José Salvador Sánchez,et al.  On the k-NN performance in a challenging scenario of imbalance and overlapping , 2008, Pattern Analysis and Applications.

[7]  Andrew K. C. Wong,et al.  Classification of Imbalanced Data: a Review , 2009, Int. J. Pattern Recognit. Artif. Intell..

[8]  Michal Wozniak.  Two-Stage Classifier for Diagnosis of Hypertension Type , 2006, ISBMDA.

[9]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Jeroen J. Bax,et al.  2007 ESH-ESC Guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). , 2007, Blood pressure.

[11]  Ethem Alpaydın,et al.  Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms , 1999, Neural Comput..

[12]  Victor Maojo,et al.  Biological and Medical Data Analysis, 6th International Symposium, ISBMDA 2005, Aveiro, Portugal, November 10-11, 2005, Proceedings , 2005, ISBMDA.

[13]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[14]  Sung-Bae Cho,et al.  Hybrid Artificial Intelligent Systems , 2015, Lecture Notes in Computer Science.

[15]  Bartosz Krawczyk,et al.  Clustering-based ensembles for one-class classification , 2014, Inf. Sci..

[16]  Bartosz Krawczyk,et al.  Diversity measures for one-class classifier ensembles , 2014, Neurocomputing.

[17]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[18]  Fabio Roli,et al.  Intrusion detection in computer networks by a modular ensemble of one-class classifiers , 2008, Inf. Fusion.

[19]  D. Mozaffarian,et al.  Executive summary: heart disease and stroke statistics--2010 update: a report from the American Heart Association. , 2010, Circulation.

[20]  Hendrik Blockeel,et al.  Knowledge Discovery in Databases: PKDD 2003 , 2003, Lecture Notes in Computer Science.

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

[22]  Xue-wen Chen,et al.  FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems , 2008, KDD.

[23]  Francisco Herrera,et al.  Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics , 2012, Expert Syst. Appl..