Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brasil: Health Surveillance Guide. Ministério da Saúde, Brasília (2014)
Andrade, V.L., Sabroza, P.C., Araújo, A.J.: Factors associated with household and family in leprosy transmission in Rio de Janeiro, Brazil. Cad. Saúde Pública 10(Suppl. 2), 281–292 (1994)
Rocha, C.A.: Characterization of the Household Contacts in a Reference Outpatient Clinic for Hanseniase in the City of Salvador-Bahia. UFBA, Salvador (2016)
Chanteau, S., Glaziou, P., Plichart, C., Luquiaud, P., Plichart, R., Faucher, J.F.: Low predictive value of PGL-I serology for the early diagnosis of leprosy in family contacts: results of a 10-year prospective field study in French Polynesia. Int. J. Lepr. 61(4), 533–541 (1993)
Barreto, J.G., et al.: High rates of undiagnosed leprosy and subclinical infection amongst school children in the Amazon Region High anti-phenolic glycolipid-I IgM titers and hidden leprosy cases, Amazon region. Mem. Inst. Oswaldo Cruz 107(Suppl. 1999), 60–67 (2012)
Gomes, G.P.: Community Health Agents as Facilitators in the Identification Process of Leprosy Patients Using Spatial Analysis. UFOPA, Santarém (2016)
Araujo, A.E., Dorlene, M.A., Goulart, I.B., Pereira, S.F., Figueiredo, I.A., Serra, H.O., Fonseca, P.A., Caldas, A.M.: Factors associated with neural alterations and physical disabilities in patients with leprosy in São Luis, State of Maranhão, Brazil. Rev. Soc. Bras. Med. Trop. 47(4), 490–497 (2014)
Castro, S.S., Abreu, G.B., Fernandes, L.F., Santos, J.P., Oliveira, V.R.: Leprosy incidence, characterization of cases and correlation with household and cases variables of the Brazilian states in 2010. An. Bras. Dermatol. 91(1), 28–33 (2016)
Araujo, S., Lobato, J., Reis, E.M., Souza, D.B., Gonçalves, M.A., Costa, A.V., Goulart, L.R., Goulart, I.B.: Unveiling healthy carriers and subclinical infections among household contacts of leprosy patients who play potential roles in the disease chain of transmission. Mem. Inst. Oswaldo Cruz 107(Suppl. 1), 55–59 (2012)
Neto, J.M., Carvalho, H.T., Cunha, L.S., Cassenote, A.F., Lozano, A.W., Martins, A.P.: Analysis of control household contacts of people affected by leprosy in Brazil and the state of São Paulo de 1991 a 2012. Hansenol. Int. 38, 68–78 (2014)
World Health Organization (WHO): Wkly. Epidemiol. Rec. 92(17), 205–228 (2017)
Sinan/SVS-MS: General detection rate of leprosy per 100,000 inhabitants: states and regions of Brazil from 1990 to 2016. Ministério da Saúde, Brasil (2017)
Frade, M.A., Paula, N., Gomes, C., Vernal, S., Bernardes, F., Lugao, H. Abreu, M., Botini, P., Duthie, M., Spencer, J.S., Soares, R.C., Foss, N.: Unexpectedly high leprosy seroprevalence detected using a random surveillance strategy in midwestern Brazil: a comparison of ELISA and a rapid diagnostic test. PLoS Negl. Trop. Dis. 11(2), 1–12 (2017)
Lastória, J.C., Abreu, M.M.: Leprosy: a review of laboratory and therapeutic aspects - Part 2. An. Bras. Dermatol. 89(3), 389–401 (2014)
Moura, R.S., Calado, K.L., Oliveira, M.L., Buhrer-Sékula, S.: Leprosy serology using PGL-I: a systematic review. Rev. Soc. Bras. Med. Trop. 41(Suplemento II), 11–18 (2008)
Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining (Adaptive Computation and Machine Learning), pp. 361–452. MIT Press, Cambridge (2001)
Webber, C., Zat, D.: Use of clustering algorithms in the educational data mining. Rev. Novas Tecnol. Educ. 11(1679–1916), 1–10 (2013)
Garner, S.R.: Weka: the Waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Student Conference, pp. 57–64. Waikato (1995)
Nogueira, A., Ferreira, M., Conde, G., Salgado, C., Barreto, J., Conde, M.: Development of a computational system in mobile devices for the optimization of the process of collection, management and analysis of data related to leprosy patients in the west of the state of Pará – Brazil. Hansenol. Int. 39(Suppl. 1), 71 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Dutra da Silva, Y.E., Salgado, C.G., Gomes Conde, V.M., Barros Conde, G.A. (2018). Data Mining Using Clustering Techniques as Leprosy Epidemiology Analyzing Model. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-93803-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93802-8
Online ISBN: 978-3-319-93803-5
eBook Packages: Computer ScienceComputer Science (R0)