Prediction of leptospirosis cases using classification algorithms

Prediction of leptospirosis cases using classification algorithms

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Software — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Leptospirosis is a potentially life-threatening disease primarily affecting low-income populations, with an estimated annual incidence of 1.03 million infections worldwide. This disease has symptoms often confused with other febrile syndromes, such as dengue fever, influenza and viral hepatitis, often making diagnosis challenging. Improving the accuracy of early diagnosis of patients with leptospirosis will increase the speed of appropriate antibiotic treatment delivery, and both will improve clinical outcomes for this potentially fatal disease. The authors conducted an analysis of clinically and epidemiologically defined leptospirosis cases to predict disease using data mining classification algorithms. They conducted four sets of experiments to evaluate the performance of the algorithms, assessing their predictive accuracy of using different training and test datasets. The JRIP algorithm achieved 84% sensitivity using a dataset of only confirmed leptospirosis cases, and a specificity of 99% using a dataset of only confirmed dengue cases. Therefore, the approach successfully predicted leptospirosis cases, differentiated them from similar febrile illnesses, and may represent a new tool to assist health professionals, particularly in endemic areas for leptospirosis, accelerating targeted treatment and minimising disease exacerbation and mortality.


    1. 1)
      • 1. Reis, R.B., Ribeiro, G.S., Felzemburgh, R.D., et al: ‘Impact of environment and social gradient on Leptospira infection in urban slums’, PLoS Negl. Trop. Dis., 2008, 2, (4), p. e228.
    2. 2)
      • 2. Brasil. Ministerio da Saude: ‘Guia de vigilancia epidemiologica / Ministerio da Saude, Secretaria de Vigilancia em Saude, Departamento de Vigilancia Epidemiologica’ (Ministerio da Saude, Brasilia, 2009, 7th edn.), p. 816– (Serie A. Normas e Manuais Tecnicos).
    3. 3)
      • 3. Brasil. Ministerio da Saude: ‘Guia de leptospirose: diagnostico e manejo clinico’. Ministerio da Saude, Secretaria de Vigilancia em Saude. Departamento de Vigilancia das Doencas Transmissiveis’ (Ministerio da Saude, Brasilia, 2014).
    4. 4)
      • 4. García-Laencina, P.J., Abreu, P.H., Abreu, M.H., et al: ‘Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values’, Comput. Biol. Med., 2015, 59, pp. 125133.
    5. 5)
      • 5. Yeh, J.Y., Wu, T.H., Tsao, C.W.: ‘Using data mining techniques to predict hospitalization of hemodialysis patients’, Decis. Support Syst., 2011, 50, (2), pp. 439448.
    6. 6)
      • 6. Yeh, D.Y., Cheng, C.H., Chen, Y.W.: ‘A predictive model for cerebrovascular disease using data mining’, Expert Syst. Appl., 2011, 38, (7), pp. 89708977.
    7. 7)
      • 7. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: ‘From data mining to knowledge discovery in databases’, AI Mag., 1996, 17, (3), p. 37.
    8. 8)
      • 8. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: ‘The KDD process for extracting useful knowledge from volumes of data’, Commun. ACM, 1996, 39, (11), pp. 2734.
    9. 9)
      • 9. Sahle, G., Meshesha, M.: ‘Uncovering knowledge that supports malaria prevention and control intervention program in Ethiopia’, Electron. J. Health Inform., 2013, 8, (1), p. 7.
    10. 10)
      • 10. Bakar, A.A., Kefli, Z., Abdullah, S., et al: ‘Predictive models for dengue outbreak using multiple rule base classifiers’. Electrical Engineering and Informatics (ICEEI), 2011 Int. Conf. on IEEE, 2011, pp. 16.
    11. 11)
      • 11. Bier, D., Molento, M.B.: ‘Distribuicao espacial e fatores de risco para leptospirose canina na Vila Pantanal, Curitiba, Parana, Brasil’. ScM thesis, Universidade Federal do Praná, 2012.
    12. 12)
      • 12. da Costa Côrtes, S., Porcaro, R.M., Lifschitz, S.: ‘Mineração de dados-Funcionalidades, técnicas e abordagens’. PUC, 2002.
    13. 13)
      • 13. Oguntimilehin, A., Adetunmbi, A.O., Abiola, O.B.: ‘A review of predictive models on diagnosis and treatment of malaria fever’, Int. J. Comput. Sci. Mobile Comput., 2015, 4, pp. 10871093.
    14. 14)
      • 14. Ugwu, C., Onyejegbu, N.L., Obagbuwa, I.C.: ‘The application of machine learning technique for malaria diagnosis’. Nigeria Computer Society 23rd National Conf., 2013, pp. 151158.
    15. 15)
      • 15. Hall, M., Frank, E., Holmes, G., et al: ‘The WEKA data mining software: an update’, ACM SIGKDD Explorations Newsletter, 2009, 11, (1), pp. 1018.
    16. 16)
      • 16. Rocha, N.Jr, Nivison, R., Barreiro, C., et al: ‘Classification model analysis for the prediction of leptospirosis cases’. Actas de la 11ª Conferencia Ibérica de Sistemas y Tecnologías de Información, Gran Canaria, España, June 2016, pp. 966971.
    17. 17)
      • 17. Ko, A.I., Reis, M.G., Dourado, C.M.R., et al , Salvador Leptospirosis Study Group: ‘Urban epidemic of severe leptospirosis in Brazil’, The Lancet, 1999, 354, (9181), pp. 820825.
    18. 18)
      • 18. Araujo, Wildo Navegantes de and Reis, Mitermayer Galvão: ‘Aspectos epidemiológicos da leptospirose no Brasil, 2000 a 2009 e a avaliação do conhecimento e das atitudes sobre a doença em uma favela na cidade de Salvador, Bahia. 114 f.’. PhD thesis, Fundação Oswaldo Cruz, Centro de Pesquisas Gonçalo Moniz, Salvador, 2010.
    19. 19)
      • 19. Frank, E., Hall, M., Holmes, G., et al: ‘Weka-a machine learning workbench for data mining’. Data mining and knowledge discovery handbook, Springer USA, 2009, pp. 12691277.
    20. 20)
      • 20. Baldi, P., Brunak, S., Chauvin, Y., et al: ‘Assessing the accuracy of prediction algorithms for classification: an overview’, Bioinformatics, 16.5, 2000, 16, (5), pp. 412424.
    21. 21)
      • 21. Lalkhen, A.G., McCluskey, A.: ‘Clinical tests: sensitivity and specificity’, Contin. Educ. Anaesth. Crit. Care Pain, 2008, 8, (6), pp. 221223.
    22. 22)
      • 22. Costa, F., Hagan, J.E., Calcagno, J., et al: ‘Global morbidity and mortality of leptospirosis: a systematic review’, PLoS Negl. Trop. Dis., 2015, 9, (9), p. e0003898.
    23. 23)
      • 23. Bergmeir, C., Hyndman, R.J., Koo, B.: ‘A note on the validity of cross-validation for evaluating time series prediction’. Monash University Department of Econometrics and Business Statistics Working Paper, 10, 15, 2015.
    24. 24)
      • 24. Dean, A.G., Arner, T.G., Sangam, S., et al: ‘Epi Info 2002, a database and statistics program for public health professionals for use on Windows 95, 98, ME, NT, 2000 and XP computers’ (Centers for Disease Control, Atlanta, 2002).

Related content

This is a required field
Please enter a valid email address