Comparison of classical machine learning and ensemble techniques in the context of dengue severity prediction

Arrubla-Hoyos, Wilson; Seveiche-Maury, Zurisaddai; Saeed, Khalid; Gómez, Jorge Eliecer Gómez; De-La-Hoz-Franco, Emiro

doi:10.1109/c358072.2023.10436288

2023 IEEE Colombian Caribbean Conference (C3) 2023

DOI: 10.1109/c358072.2023.10436288

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Comparison of classical machine learning and ensemble techniques in the context of dengue severity prediction

Wilson Arrubla-Hoyos,

Zurisaddai Seveiche-Maury,

Khalid Saeed

et al.

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2024

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Cited by 2 publications

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Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Arrubla-Hoyos,

Gómez,

De-La-Hoz-Franco

2024

Informatics

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Dengue, Zika, and chikungunya viruses pose a serious threat globally and circulate widely in America. These diseases share similar symptoms in their early stages, which can make early diagnosis difficult. In this study, two predictive models based on Decision Trees and Random Forests were developed to classify dengue, Zika, and chikungunya, with the aim of being supportive and easily interpretable for the medical community. To achieve this, a dataset was collected from a clinic in Sincelejo, Colombia, including the signs, symptoms, and laboratory results of these diseases. The Pan American Health Organization (PAHO) Diagnostic Guide 2022 methodology for the differential classification of dengue and chikungunya was applied by assigning evaluative weights to symptoms in the dataset. In addition, a bootstrapping resampling technique based on the central limit theorem was used to balance the target variable, and cross-validation was used to train the models. The main results were obtained with the Random Forest technique, achieving an accuracy of 99.7% for classifying chikungunya, 99.1% for dengue, and 98.8% for Zika. This study represents a significant advance in the differential prediction of these diseases through the use of automatic learning techniques and the integration of clinical and laboratory information.

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Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Arrubla-Hoyos,

Gómez,

De-La-Hoz-Franco

2024

Informatics

View full text Add to dashboard Cite

show abstract

Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide

Arrubla-Hoyos,

Gómez,

De-La-Hoz-Franco

2024

Viruses

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Arboviruses such as dengue, Zika, and chikungunya present similar symptoms in the early stages, which complicates their differential and timely diagnosis. In 2022, the PAHO published a guide to address this challenge. This study proposes a methodological framework that transforms qualitative information into quantitative information, establishing differential weights in relation to symptoms according to the medical evidence and the GRADE scale based on recommendation 1 of the said guide. To achieve this, common variables from the dataset were identified using the PAHO guide, and quality rules were established. A linear interpolation function was then parameterised to assign weights to the symptoms according to the evidence. Machine learning was used to compare the different models, achieving 99% accuracy compared with 79% without the methodology. This proposal represents a significant advancement, allowing the direct application of the PAHO recommendations to the dataset and improving the differential classification of arboviruses.

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Comparison of classical machine learning and ensemble techniques in the context of dengue severity prediction

Cited by 2 publications

References 17 publications

Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide

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