Applications of machine learning techniques to predict filariasis using socio-economic factors.

Abstract:

:Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.

journal_name

Epidemiol Infect

authors

Kondeti PK,Ravi K,Mutheneni SR,Kadiri MR,Kumaraswamy S,Vadlamani R,Upadhyayula SM

doi

10.1017/S0950268819001481

subject

Has Abstract

pub_date

2019-09-02 00:00:00

pages

e260

eissn

0950-2688

issn

1469-4409

pii

S0950268819001481

journal_volume

147

pub_type

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