Classification of Dengue Haemorrhagic Fever (DHF) using SVM, naive bayes and random forest

Arafiyah, Ria; Hermin, Fariani; Kartika, Irma Ratna; Alimuddin, Alimuddin; Saraswati, Irma

doi:10.1088/1757-899x/434/1/012070

Cited by 17 publications

(49 citation statements)

References 3 publications

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“…Others have developed dengue diagnostic clinical algorithms with Bayesian networks which reached 73% sensitivity 64 and Bayesian naive systems yielding sensitivities between 72% and 99%. [65][66][67] Bayesian approaches have the advantage that they reflect the "natural" diagnostic process and quantify the associated uncertainty in the diagnosis of dengue as a predictive probability. Precisely to reflect real-life conditions, the prior probability of dengue in the prospective validation was not fixed.…”

Section: Discussionmentioning

confidence: 99%

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia

Caicedo-Borrero

Tovar

Méndez

et al. 2020

The American Journal of Tropical Medicine and Hygiene

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Diagnosing dengue in endemic areas remains problematic because of the low specificity of the symptoms and lack of accurate diagnostic tests. This study aimed to develop and prospectively validate, under routine care, dengue diagnostic clinical algorithms. The study was carried out in two phases. First, diagnostic algorithms were developed using a database of 1,130 dengue and 918 non-dengue patients, expert opinion, and literature review. Algorithms with > 70% sensitivity were prospectively validated in a single-group quasi-experimental trial with an adaptive Bayesian design. In the first phase, the algorithms that were developed with the continuous Bayes formula and included leukocytes and platelet counts, in addition to selected signs and symptoms, showed the highest sensitivities (> 80%). In the second phase, the algorithms were applied on admission to 1,039 consecutive febrile subjects in three endemic areas in Colombia of whom 25 were laboratory-confirmed dengue, 307 non-dengue, 514 probable dengue, and 193 undetermined. Including parameters of the hemogram consistently improved specificity without affecting sensitivity. In the final analysis, considering only confirmed dengue and non-dengue cases, an algorithm with a sensitivity and specificity of 65.4% (95% credibility interval 50-83) and 40.1% (34.7-45.7) was identified. All tested algorithms had likelihood ratios close to 1, and hence, they are not useful to confirm or rule out dengue in endemic areas. The findings support the use of hemograms to aid dengue diagnosis and highlight the challenges of clinical diagnosis of dengue.

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Section: Discussionmentioning

confidence: 99%

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia

Caicedo-Borrero

Tovar

Méndez

et al. 2020

The American Journal of Tropical Medicine and Hygiene

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“…Although these three are common arboviral diseases, no studies were found that carried out multi-classification considering these arboviral diseases or other arboviral types, such as West Nile virus, yellow fever virus, Saint Louis Encephalitis virus, Mayaro virus, Oropouche virus and others, showing that there are space for further investigations in this area. Most of works presented models for binary classification: Dengue or not [40][41][42][43][44][45]; DHF or not [46]; Chikungunya or not [47]; and Zika classified between "Discarded cases" and "Somewhat probable" for CZS [48]. It is interesting to note that the only work that deals with Zika is focused on CZS, not covering Zika in general.…”

Section: What Arboviruses Are the Focus Of Research On Machinementioning

confidence: 99%

“…SVM models can also be quite memory-intensive and therefore processing large and complex data sets can be slow [57]. SVM models feature in five studies in the sample - [41], [46], [44], [47] and [50].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The usage of different data sets and lack of detail regarding both model configuration, feature selection, and hyperparameter optimisations made comparisons of these studies difficult. For example, six of the 12 studies [41,42,44,46,51,56] did not present any description of their proposed models thus adversely impacting future reproducibility.…”

Section: Denguementioning

confidence: 99%

“…Arafiyah et al [46] used a medical data set comprising 213 records using only four clinical data sources -temperature, presence of spotting, presence of bleeding, and tornikuet test. The complete list of the attributes present in the data set was not described by the authors, so there is no way to know if or how feature selection was applied.…”

Section: Summary Of Data Sets and Attributesmentioning

confidence: 99%

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

Neto¹,

Oliveira²,

Teixeira³

et al. 2021

Preprint

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Background: NTDs primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on ML and DL models. Method: We carried out a SLR in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and 7 from single backward snowballing technique), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using Tree based ML algorithms and only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its levels) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life.

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Performance evaluation of artificial neural networks for feature extraction from dengue fever

Ergu

2022

Cogent Engineering

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Classification of Dengue Haemorrhagic Fever (DHF) using SVM, naive bayes and random forest

Cited by 17 publications

References 3 publications

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia

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

Performance evaluation of artificial neural networks for feature extraction from dengue fever

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