Remote Sensing and Geographic Information System, Asian Institute of Technology, Pathum Thani, Thailand.
Disaster Preparedness Mitigation and Management, Asian Institute of Technology, Pathum Thani, Thailand.
Int J Environ Health Res. 2022 Aug;32(8):1716-1732. doi: 10.1080/09603123.2021.1905782. Epub 2021 Mar 26.
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.
本研究调查了气候因素对印度奥里萨邦孙德尔本斯地区疟疾发病率的影响。使用 WEKA 机器学习工具和两种分类器技术，多层感知器（MLP）和 J48，以及三种测试选项，即 10 折交叉验证、百分位分割和提供的测试，进行了对比分析，以确定在不同气候环境下，疟疾预测准确性技术中更优的模型。结果表明，在百分位分割和提供的测试选项中，J48 比 MLP 更适合使用 10 折交叉验证方法，其错误率较低（RMSE=0.6）、kappa 值较高（kappa=0.63）、准确性较高（accuracy=0.71），表明它是最合适的模型。温度和湿度的季节性变化与疟疾事件的相关性比降雨更好，而且在季风和季风后时期，当事件达到峰值时，表现更好。
