Abstract
The study, double-blinded anti-leprosy vaccination trial, the dependent variable is categorical and dichotomous (diseased and non-diseased), the classifier CART was suggested to identify persuading factors for diagnosing leprosy during first and second resurveys. We constructed a predictive model for predicting leprosy of both resurveys and compared these values with the actual outcomes of consecutive resurveys. The independent variables like age, sex, symptoms, vaccination status, etc., after vaccination were used for constructing logistic regression, classification and regression tree models. The CART model is used to identify the influencing predictors (independent factors) for leprosy diagnosis. We had evaluated sensitivity (95% CI, ranged from 99 to 100%), specificity (95% CI, from 97 to 100%), positive predictive values (95% CI, from 61.43 to 61.64%) and negative predictive values (95% CI, 99%) and compared the performance of the above suggested models. The CART model was suggested as a cost-effective tool and appropriate classifiers for large prospective cohort studies such as the current anti-leprosy trial.
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
Kanaya AM, Glidden DV, Chambers HF (2001) Identifying pulmonary tuberculosis in patients with negative sputum smear results. Chest 120:349–355
Wang C-S, Chen H-C, Chong I-W, Hwang J-J, Huang M-S (2008) Predictors for identifying the most infectious pulmonary tuberculosis patient. J Formos Med Assoc 107(1):13–20
Hammermeister KE, De Rouen TA, Dodge HT (1959) Variables predictive of survival in patients with coronary disease selection by univariate and multivariate analyses from the clinical, electrocardiographic, exercise, arteriographic, and quantitative angiographic evaluations. Circulation 59(3):421–430
Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97:1837–1847
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth Books, 358
de Queiroz Mello FC, do Valle Bastos LG, Soares SLM, Rezende VMC, Conde MB, Chaisson RE, Kritski AL, Ruffino-Netto A, Werneck GL (2006) Predicting smear negative pulmonary tuberculosis with classification trees and Logistic regression: a cross-sectional study. BioMed Central Public Health 6(43):1–8
Chaurasia V, Pal S (2013) Early prediction of heart diseases using data mining techniques. Carib J Sci Technol 1:208–217
Bhushan P, Kabirsardana S, Koranne RV, Choudhary M, Manjul P(2008) Diagnosing multibacillary leprosy: a comparative evaluation of diagnostic accuracy of slit-skin smear, bacterial index of granuloma and WHO operational classification. Indian J Dermatol Venereol Leprol 74(4):322–326
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Venmani, A., Ramakrishnan, R. (2021). Identifying Persuading Factors for Diagnosing Leprosy Using Classifier CART. In: Mahapatra, R.P., Panigrahi, B.K., Kaushik, B.K., Roy, S. (eds) Proceedings of 6th International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 177. Springer, Singapore. https://doi.org/10.1007/978-981-33-4501-0_5
Download citation
DOI: https://doi.org/10.1007/978-981-33-4501-0_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4500-3
Online ISBN: 978-981-33-4501-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)