透過多種因子預測隔週登革熱的感染區域

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姓名

鄭琮翰(Cong-Han Zheng) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

透過多種因子預測隔週登革熱的感染區域
(Predicting infection area of Dengue for next week through a variety of factors)

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至系統瀏覽論文 (2026-7-1以後開放)

摘要(中)

因為登革熱需要二次感染才會造成重症的特性，死亡率不高，造成疫情的嚴峻程度較不起眼，但隨著全球暖化，登革熱的分布範圍漸漸改變；過去國內外對於登革熱的研究中，最常見的是以氣候因子做登革熱的預測，再來是氣候因子結合社會因子與氣候因子結合地理因子，目前並無將三種因子結合的研究，故本研究結合三種因子，與臺灣登革熱的病例資料，以人口數量劃分的二級區域做為顆粒度，利用 Random Forest 與XGBoost 建立登革熱隔週感染區域預測模型。最後實驗結果 Random Forest 與 XGBoost的 ROC/AUC 皆高於 93%，且 Recall 皆高於 80%，依照此結果，政府單位可以更精準的去判別需要噴藥撲滅的登革熱可能感染區域，進而降低人力成本與醫療資源。

摘要(英)

Death rate of dengue fever is low, because dengue fever become severe illness only when second infection happened. However, global warming is getting server recently, which make the infection distribution of dengue fever different. Common method of previous studies use climate factors combined with social or geographic factors to predict dengue fever. However, recent study did not use combination of these three factors into dengue fever prediction. We proposed a method that combines these three factors with data of Taiwanese dengue fever and uses the secondary area divided by the population as the granularity. Random Forest and XGBoost are used for prediction model of weekly dengue fever infection area.

Experimental results showed that the ROC/AUC of Random Forest and XGBoost are both higher than 93% The Recall rate is higher than 80%. With the result, government can determine which area should do disinfection process to reduce the infection rate of dengue infection. Because of accurate prediction and disinfection process, the personnel cost can be reduced and it can prevent waste of medical recourse.

關鍵字(中)

★ 登革熱
★ 不平衡資料
★ 隨機森林
★ 極限梯度提升

關鍵字(英)

★ Dengue
★ Random Forest
★ XGBoost,
★ imbalanced data

論文目次

目錄
摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1-1 研究背景 1
1-2 研究動機 3
1-3 研究目的 3
1-4 研究架構 5
第二章文獻探討 6
2-1 臺灣登革熱之研究 6
2-2 機器學習於登革熱之研究 7
2-3 Random Forest 9
2-4 XGBoost 10
第三章研究方法 11
3-1 研究流程 11
3-2 研究特徵 12
3-2-1 登革熱病例相關特徵 12
3-2-2 氣候因子相關特徵 13
3-2-3 社會因子相關特徵 14
3-2-4 地理因子相關特徵 15
3-3 Random Forest 16
3-4 XGBOOST 17
3-5 模型評分介紹 20
3-5-1 混淆矩陣(Confusion Matrix) 21
3-5-2 ROC/AUC 22
3-5-3 F1-Score 23
第四章研究實驗 24
4-1 資料來源與蒐集 24
4-1-1 登革熱感染資料 24
4-1-2 氣候因子資料 24
4-1-3 社會因子與地理因子資料 24
4-2 資料前處理 25
4-2-1 資料標準化 25
4-2-2 資料切割 26
4-2-3 資料平衡 26
4-3 模型參數調整 28
4-3-1 Random Forest 參數調整 28
4-3-2 XGBoost參數調整 30
4-4 實驗結果與分析 32
4-4-1 氣候因子建立模型預測結果 32
4-4-2 氣候因子與社會因子建立模型預測結果 33
4-4-3 氣候因子與地理因子建立模型預測結果 33
4-4-4 結合三種因子之預測模型結果 34
4-5 模型重要特徵 37
4-5-1 Random Forest 模型重要特徵 38
4-5-2 XGBoost 模型重要特徵 39
4-6 調整後模型結果與分析 40
第五章結論 41
5-1 研究結論 41
5-2 研究限制與未來研究建議 43
參考文獻 44

參考文獻

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指導教授

許秉瑜

審核日期

2021-7-20

推文