Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review

SR da Silva Neto, T Tabosa Oliveira… - PLoS neglected …, 2022 - journals.plos.org
Background Neglected tropical diseases (NTDs) primarily affect the poorest populations,
often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a …

A comparative study of machine learning techniques for multi-class classification of arboviral diseases

T Tabosa de Oliveira, SR da Silva Neto… - Frontiers in Tropical …, 2022 - frontiersin.org
Among the neglected tropical diseases (NTDs), arboviral diseases present a significant
number of cases worldwide. Their correct classification is a complex process due to the …

Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

I Ozer, O Cetin, K Gorur, F Temurtas - Neural Computing and Applications, 2021 - Springer
The prediction of hospital patients and outpatients with suspected arboviral infection
individuals in research-limited settings of the urban areas is defined as a challenging …

A systematic review of applications of machine learning and other soft computing techniques for the diagnosis of tropical diseases

K Attai, Y Amannejad, M Vahdat Pour, O Obot… - Tropical Medicine and …, 2022 - mdpi.com
This systematic literature aims to identify soft computing techniques currently utilized in
diagnosing tropical febrile diseases and explore the data characteristics and features used …

Arboviral disease record data-dengue and chikungunya, brazil, 2013–2020

SR da Silva Neto, T Tabosa de Oliveira, IV Teixiera… - Scientific data, 2022 - nature.com
One of the main categories of Neglected Tropical Diseases (NTDs) are arboviruses, of which
Dengue and Chikungunya are the most common. Arboviruses mainly affect tropical …

Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

R Sippy, DF Farrell, DA Lichtenstein… - PLoS neglected …, 2020 - journals.plos.org
Background Dengue, chikungunya, and Zika are arboviruses of major global health
concern. Decisions regarding the clinical management of suspected arboviral infection are …

Alternating decision trees for early diagnosis of dengue fever

MN Kumar - arXiv preprint arXiv:1305.7331, 2013 - arxiv.org
Dengue fever is a flu-like illness spread by the bite of an infected mosquito which is fast
emerging as a major health problem. Timely and cost effective diagnosis using clinical and …

A new intelligence-based approach for computer-aided diagnosis of dengue fever

VSH Rao, MN Kumar - IEEE transactions on information …, 2011 - ieeexplore.ieee.org
Identification of the influential clinical symptoms and laboratory features that help in the
diagnosis of dengue fever (DF) in early phase of the illness would aid in designing effective …

Comparing machine learning with case-control models to identify confirmed dengue cases

TS Ho, TC Weng, JD Wang, HC Han… - PLoS neglected …, 2020 - journals.plos.org
In recent decades, the global incidence of dengue has increased. Affected countries have
responded with more effective surveillance strategies to detect outbreaks early, monitor the …

Sensitivity and specificity of a novel classifier for the early diagnosis of dengue

NM Tuan, HT Nhan, NVV Chau, NT Hung… - PLoS neglected …, 2015 - journals.plos.org
Background Dengue is the commonest arboviral disease of humans. An early and accurate
diagnosis of dengue can support clinical management, surveillance and disease control and …