Raspberry Pi-Based Medical Expert System for Pre-Diagnosis of Mosquito-Borne Diseases

Magwili, Glenn V.; Latina, Mary Ann E.; Miguel, Freyja Ivorie C.; Ortega, Jayne Kazandra P.; Pastoril, Tyrone Kent L.; Tanglao, Eunice Jerelle D.

doi:10.1109/hnicem.2018.8666364

Cited by 2 publications

(1 citation statement)

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“…The results obtained from the study were compared with that of conventional and intuitionist fuzzy projection and found to covary strongly. Magwili et al [ 22 ] provided a preliminary diagnosis for patients suffering from mosquito-borne diseases by comparing the system’s preliminary diagnosis with the expert’s diagnosis in a total of 80 tests with 20 tests per disease; 71.67%, 83.33%, and 91.67% of the time, the system correctly prediagnosed dengue, chikungunya, and malaria, respectively. For other diseases, the system correctly identified the unlikelihood of having the said mosquito-borne diseases 91.67% of the time.…”

Section: Related Literaturementioning

confidence: 99%

Modelling Differential Diagnosis of Febrile Diseases with Fuzzy Cognitive Map

Obot

John²,

Udo

et al. 2023

TropicalMed

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The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases. With 2465 datasets gathered from four states in the febrile diseases-prone regions in Nigeria with the aid of 60 medical doctors, 10 of those doctors helped in weighting and fuzzifying the symptoms, which were used to generate the FCM model. Results obtained from computations to predict diagnosis results for the 2465 patients, and those diagnosed by the physicians on the field, showed an average of 87% accuracy for the 11 febrile diseases used in the study. The number of comorbidities of diseases with varying degrees of severity for most patients in the study also covary strongly with those found by the physicians in the field.

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Section: Related Literaturementioning

confidence: 99%