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Utilization of machine learning for dengue case screening

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Abstract
Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making
it a significant public health concern. To address this, artificial intelligence tools like machine learning can play
a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies
relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy
of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years
2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual
information technique was used to assess which variables were most related to laboratory-confirmed dengue
cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the
dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify
the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache,
vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models
achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be
suitable for building an application and implementing it on smartphones. This resource would be available to
healthcare professionals such as doctors and nurses.
Keywords Arboviruses, Artificial intelligence, Clinical signs, Healthcare systems

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Judul Seri

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No. Panggil

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Penerbit

: .,

Deskripsi Fisik

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Bahasa

Indonesia

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Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

JURNAL

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Info Detail Spesifik

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Pernyataan Tanggungjawab

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