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.

摘要

本研究调查了印度奥里萨邦 Sundargarh 地区气候因素对疟疾发病率的影响。WEKA 机器学习工具与两种分类器技术、多层感知器 (MLP) 和 J48 一起使用，具有三个测试选项、10 折交叉验证、百分位数分割和提供的测试。进行了比较分析，以确定在不同气候背景下疟疾预测准确度技术的优越模型。结果表明，J48 在百分位分割和提供的测试选项上使用 10 倍交叉验证方法表现出比 MLP 更好的技能。J48 表现出更少的误差（RMSE = 0.6），更好的 kappa = 0.63，更高的准确度 = 0.71），表明它是最合适的模型。