Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha

ABSTRACT

This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak.

KEYWORDS:

• Machine learning
• malaria prediction
• J48 decision tree
• multilayer perceptron

Highlights

• The researchers analyzed the data using machine learning methods and quantify the accuracy and skill level for predicting the malaria incidences

• Among the Weka classifiers used, J48 exhibited better skill than MLP and illustrated less error and positive kappa and higher accuracy.

• 10-fold cross-validation method had better performance over the percentile split and supplied test options.

• Seasonal temperature and humidity variation had shown a better association with malaria incidents in comparison to rainfall.

• Results are encouraging for the prospect of the utilization of climate forecast for prediction of malaria incidences on a seasonal scale, which is not yet available in the region

Acknowledgments

The authors would like to acknowledge the Directorate of Public Health’s contribution, Government of Odisha, for providing the malaria incident datasets. Also, the ECMWF for keeping the climate datasets free and open for public access.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available as they are collected from government sources and due to the sensitivity but are available from the corresponding author on reasonable request.

Disclosure statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Funding

The authors have no funding to report.