Advanced Analytics Engineering Centre, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia.
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan, Kampus Kota Bharu, Lembah Sirrh, 15050, Kota Bharu, Kelantan, Malaysia.
Sci Rep. 2021 Jan 13;11(1):939. doi: 10.1038/s41598-020-79193-2.
Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980's, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
登革热是一种由蚊子传播的疾病，影响着全球近 39 亿人。自 20 世纪 80 年代在马来西亚爆发以来，登革热一直流行于马来西亚，其病例高发区集中在雪兰莪州。登革热爆发的预测因素可以为卫生官员提供及时的信息，以便采取预防措施。在这项研究中，评估了马来西亚雪兰莪州五个在 2013 年至 2017 年期间显示出最高登革热发病率的地区，以确定最佳的机器学习模型来预测登革热爆发。每个模型都使用了气候变量，如温度、风速、湿度和降雨量。基于研究结果，SVM（线性核）表现出最佳的预测性能（准确率=70%，灵敏度=14%，特异性=95%，精准率=56%）。然而，与不平衡数据（原始数据）相比，SVM（线性）的测试样本的灵敏度从 14%增加到 63.54%。一年中的周数是 SVM 模型中最重要的预测因素。本研究表明，机器学习在登革热爆发预测方面具有可观的潜力。未来的研究应该考虑使用提升算法或基于自然启发式算法来开发登革热预测模型。
