Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data
Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data
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DOI：
10.3389/feart.2020.00377
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发表时间：
2020-11-19
影响因子：
2.9
通讯作者：
Masrani, Afiqah
中科院分区：
文献类型：
--
作者：
Rahmat, Fariq;Zulkafli, Zed;Masrani, Afiqah
钩端螺旋体病在世界各地的爆发与天气变化有关。此外，已经证明这些影响会在长达10个月的不同滞后期发生，影响预测钩端螺旋体病发生的模拟模型的性能。在马来西亚，尽管近年来病例越来越多，但不同时间滞后的不同天气参数之间的联系尚未建立。在这项研究中，数据挖掘和机器学习的组合被用来分析，捕获和预测钩端螺旋体病的发生与温度，降雨量和相对湿度之间的关系，使用塞伦班区在马来西亚作为案例研究。首先，降雨的最佳时间滞后确定使用图形探索性数据分析（EDA），而非图形EDA用于温度。然后，开发了一个人工神经网络（ANN）模型，使用反向传播训练，优化隐藏层和隐藏节点的数量，将所选特征的组合分类为疾病发生和不发生。使用每个模型的准确性、灵敏度和特异性来衡量成功。EDA表明，塞伦班钩端螺旋体病的发生与滞后16周的周平均温度和滞后12-20周的周降雨量高度相关。使用这些选定的功能，人工神经网络模型实现了最高的准确性，灵敏度和特异性，分别为84.00%，86.44%和79.33%。总体而言，EDA方法将预测模型的准确性从基线模型提高了13.30-31.26%。
Leptospirosis outbreaks in various parts of the world have been linked to changes in the weather. Furthermore, the effects have been shown to occur at different lags of up to 10 months, affecting the performance of simulation models that predict leptospirosis occurrence. In Malaysia, the link between different weather parameters, at different time lags, has yet to be established despite an increasing number of cases in recent years. In this study, a combination of data mining and machine learning is used to analyze, capture, and predict the relation between leptospirosis occurrence and temperature, rainfall, and relative humidity using the Seremban district in Malaysia as a case study. First, the optimal time lags for rainfall were determined using graphical exploratory data analysis (EDA) while non-graphical EDA was used for temperature. Then, an artificial neural network (ANN) model is developed to classify the combination of selected features into disease occurrence and non-occurrence using back-propagation training, optimizing the number of hidden layers and hidden nodes. The success is measured using accuracy, sensitivity, and specificity of each model. EDA has shown that leptospirosis occurrence in Seremban is highly correlated with weekly average temperature at lag 16 weeks and weekly rainfall amount at lag 12-20 weeks. Using these selected features, the ANN model achieved the highest accuracy, sensitivity, and specificity at 84.00, 86.44, and 79.33%, respectively. Overall, the EDA approach has increased the accuracy of the predictive model by 13.30-31.26% from the baseline models.