01813nas a2200265   4500000000100000008004100001260004400042653001500086653001600101653001700117653002100134653001200155100001000167700001000177700001100187700002200198700001500220700001900235700001200254245008500266300001200351520114800363022001401511020002201525       2018                            d  bSpringer International PublishingaCham10aclustering10aData mining10aEpidemiology10aKohonen networks10aleprosy1 aTan Y1 aShi Y1 aTang Q1 aDutra da Silva YE1 aSalgado CG1 aGomes Conde VM1 aConde G00aData mining using clustering techniques as leprosy epidemiology analyzing model.  a284-2933 a<p>Leprosy remains a public health problem in the world and also in Brazil. The people’s living conditions, especially of the most socially vulnerable, dramatically influence the risk of contagion of the disease. In this context, this study aimed to analyze the epidemiology of leprosy through the list of patients and the environment of these using data mining techniques with clustering methods. In the process of creating of clusters, best results were obtained with Self-Organizing Maps of Kohonen with information organized into 6 clusters. A set of data with SINAN patients and new cases of leprosy found in an active search carried out in the municipality of Santarém in the year 2014. The results were analyzed, draws attention the values found for the Anti PGL-1 in cluster 4 first set of data analysis which indicates very high values of positive, indicating a high load of the leprosy bacillus, and therefore a high risk for communicating. The study demonstrated that the identification of leprosy patient’s relationship profile with your family and your household appear as promising tools like leprosy control strategy.</p>
  a0302-9743  a978-3-319-93802-8