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 Infectjournal_title
Epidemiology and infectionauthors
Kondeti PK,Ravi K,Mutheneni SR,Kadiri MR,Kumaraswamy S,Vadlamani R,Upadhyayula SMdoi
10.1017/S0950268819001481subject
Has Abstractpub_date
2019-09-02 00:00:00pages
e260eissn
0950-2688issn
1469-4409pii
S0950268819001481journal_volume
147pub_type
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