Malaysia Dengue Outbreak Detection Using Data Mining Models

This paper presents the Malaysian dengue outbreak detection model using three classification methods. Dengue outbreak detection and prediction has been a major interest of researchers in surveillance and public health. In this paper, a selection of different dengue data attributes are used for classification modeling and the performances are compared with the previous related work. The experimental results show that the proposed classifiers improve the performance of other methods. The significant selection of attributes in dengue dataset contributes to the good results.

[1]  Yang Bingru,et al.  Rough Set Based Classification rules generation for SARS Patients , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[2]  Shahrul Azman Mohd Noah,et al.  Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network , 2010 .

[3]  K. Higuchi,et al.  Automatic Prediction System of Dengue Haemorrhagic-Fever Outbreak Risk by Using Entropy and Artificial Neural Network , 2008, 2008 International Symposium on Communications and Information Technologies.

[4]  Jian Pei,et al.  Classification: Basic Concepts , 2012 .

[5]  Mark J. Schreiber,et al.  Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness , 2008, PLoS neglected tropical diseases.

[6]  Azuraliza Abu Bakar,et al.  A Comparative Study for Various Methods of Classification , 2012 .

[7]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[8]  J. Amini.  OPTIMUM LEARNING RATE IN BACK-PROPAGATION N EURAL NETWORK FOR CLASSIFICATION OF SATELLITE IMAGES (IRS-ID) , 2008 .

[9]  Aleksander Øhrn.  ROSETTA Technical Reference Manual , 2001 .

[10]  Vadrevu Sree Hari Rao,et al.  A New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever , 2015, IEEE Transactions on Information Technology in Biomedicine.

[11]  Fatimah Ibrahim,et al.  A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN) , 2005, Comput. Methods Programs Biomed..

[12]  G. Guthrie,et al.  Detection of disease outbreaks in pharmaceutical sales: neural networks and threshold algorithms , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[13]  Z. Abd Latif,et al.  Impact of climate and landuse variability based on dengue epidemic outbreak in Subang Jaya , 2011, 2011 IEEE Colloquium on Humanities, Science and Engineering.

[14]  Jonathan M. Garibaldi,et al.  A Comparison of Three Different Methods for Classification of Breast Cancer Data , 2008, 2008 Seventh International Conference on Machine Learning and Applications.

[15]  Azuraliza Abu Bakar,et al.  Predictive models for dengue outbreak using multiple rulebase classifiers , 2011, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics.

[16]  Azuraliza Abu Bakar,et al.  Negative selection algorithm for dengue outbreak detection , 2013 .

[17]  Azuraliza Abu Bakar,et al.  Multiple Attribute Frequent Mining-Based for Dengue Outbreak , 2010, ADMA.

[18]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[19]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[20]  Nisar Hundewale,et al.  A Review of Data Mining Classification Techniques Applied for Diagnosis and Prognosis of the Arbovirus-Dengue , 2011 .

[21]  M. J. Diamantopoulou,et al.  Performance Evaluation of Artificial Neural Networks in Estimating REFERENCE EVAPOTRANSPIRATION WITH MINIMAL METEOROLOGICAL DATA , 2011 .

[22]  Gams Matjaz,et al.  Data Mining Techniques for Explaining Social Events , 2011 .

[23]  Mohd Talib Latif,et al.  PERUBAHAN CUACA DAN PENYAKIT DENGGI: KAJIAN KES DI DAERAH SEREMBAN, NEGERI SEMBILAN, MALAYSIA , 2011 .

[24]  Koh Hock Lye,et al.  Modeling Dengue Fever Subject to Temperature Change , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[25]  Fred Collopy,et al.  How effective are neural networks at forecasting and prediction? A review and evaluation , 1998 .