Learning of High Dengue Incidence with Clustering and FP-Growth Algorithm using WHO Historical Data

Franz Stewart V. Dizon; Stephen Kyle R. Farinas; Reynaldo John Tristan H. Mahinay Jr.; Harry S. Pardo; Cecil Jose A. Delfinado

arXiv · 1901.11376

Learning of High Dengue Incidence with Clustering and FP-Growth Algorithm using WHO Historical Data

Franz Stewart V. Dizon·Stephen Kyle R. Farinas·Reynaldo John Tristan H. Mahinay Jr.·Harry S. Pardo·Cecil Jose A. Delfinado

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Abstract

This paper applies FP-Growth algorithm in mining fuzzy association rules for a prediction system of dengue. The system mines its rules through input of historic predictor variables for dengue. The rules will be used to build a rule-based classifier to predict the dengue incidence for the next month for the years 2001-2006 in the Philippines. The FP-Growth Algorithm was compared to Apriori Algorithm by Sensitivity, Specificity, PPV, NPV, execution time and memory usage. The results showed that FP-Growth Algorithm is significantly better in execution time, numerically better in memory and comparable in Sensitivity, Specificity, PPV and NPV to Apriori Algorithm.

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Franz Stewart V. Dizon, Stephen Kyle R. Farinas, Reynaldo John Tristan H. Mahinay Jr., Harry S. Pardo, Cecil Jose A. Delfinado. 2019-01-12. Learning of High Dengue Incidence with Clustering and FP-Growth Algorithm using WHO Historical Data. https://arxiv.org/abs/1901.11376

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