Risk Factors Associated With Dengue Fever Outbreak in Taiz Governorate, Yemen, 2018: Case-control Study

Nassar, Abdulkareem Ali Hussein; Torbosh, Amr; Mahyoub, Yassin Abdulmalik; Amad, Mohammed Abdullah Al

doi:10.21203/rs.3.rs-624873/v1

Cited by 1 publication

(1 citation statement)

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“…In another study on dengue disease, dengue viruses can infect more than 3.9 billion people in 128 countries [3]. As stated by the Healthcare sector in Taiz city, Yemen, 4,770 cases of dengue fever had been detected in Taiz governorate in the southwestern part of the country from the beginning of the year through the end of July 2021 [4].…”

Section: Introductionmentioning

confidence: 99%

Early Diagnosis for Dengue Disease Prediction Using Efficient Machine Learning Techniques Based on Clinical Data

Abdualgalil

Abraham

Ismael

2022

Journal of Robotics and Control

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Dengue fever is a worldwide issue, especially in Yemen. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. Thus, this issue necessitates the development of a new diagnostic schema. The objective of this work is to develop a diagnostic model for the earlier diagnosis of dengue disease using Efficient Machine Learning Techniques (EMLT). This paper proposed prediction models for dengue disease based on EMLT. Five different efficient machine learning models, including K-Nearest Neighbor (KNN), Gradient Boosting Classifier (GBC), Extra Tree Classifier (ETC), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM). All classifiers are trained and tested on the dataset using 10-Fold Cross-Validation and Holdout Cross-Validation approaches. On a test set, all models were evaluated using different metrics: accuracy, F1-sore, Recall, Precision, AUC, and operating time. Based on the findings, the ETC model achieved the highest accuracy in Hold-out and 10-fold cross-validation, with 99.12 % and 99.03 %, respectively. In the Holdout cross-validation approach, we conclude that the best classifier with high accuracy is ETC, which achieved 99.12 %. Finally, the experimental results indicate that classifier performance in holdout cross-validation outperforms 10-fold cross-validation. Accordingly, the proposed dengue prediction system demonstrates its efficacy and effectiveness in assisting doctors in accurately predicting dengue disease.

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Section: Introductionmentioning

confidence: 99%