Expert System for Early Diagnosis of COVID - 19

Dass, Sharana Dharshikgan Suresh; Meskaran, F.; Saeedi, Mitra

doi:10.31782/ijcrr.2020.122227

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The test results of experimental cases calculated using Nave Bayes resulted in a PDP diagnosis with a value of 0.3400 or 34%, the nave Bayes classifier method was successfully implemented for the PDP, ODP and OTG categories [26]. Other research related to the author's research conducted by [27] The expert system for early diagnosis of COVID-19 which was built using the Fuzzy Expert System was successfully used in early diagnosis of COVID-19 with positive and non-positive diagnostic results, making it easier for doctors to diagnose the symptoms experienced.…”

Section: Introductionmentioning

confidence: 97%

Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19

Nurdin,

Susanti,

Aidilof

et al. 2022

matrik

View full text Add to dashboard Cite

COVID-19 is a respiratory infection disease caused by the corona virus. Transmission of this virus can spread very quickly so that the number of cases of the corona virus continues to grow and becomes an epidemic that spreads not only in Indonesia but also in other countries in the world. The purpose of this study is to build an expert system that is able to diagnose Covid-19 early by using a comparison of the Nave Bayes method and the Dempster Shafer method. The amount of data used in this study is 550 data, consisting of 500 training data and 50 testing data. While the variables used are symptoms related to COVID-19 as many as 17 symptoms consisting of G01, G02, G03, G04, G05, G06, G07, G08, G09, G10, G11, G12, G13, G14, G15, G16, G17. The diagnostic data consists of Suspected (PDP), Non-Suspected, and Close Contact (ODP). The results of the percentage test by comparing system diagnoses with expert diagnoses, for the nave Bayes method it has an accuracy of 96% with 48 diagnoses according to expert diagnoses from 50 tested data. Meanwhile, the Dempster Shafer method has an accuracy of 40% with 20 diagnoses according to expert diagnoses from 50 tested data. Based on the results of this study, the Naive Bayes and Dempster Shafer methods can be applied to an expert system for early diagnosis of COVID-19, from the results of the system testing the Naive Bayes method has better accuracy than the Dempster Shafer method.

show abstract

Section: Introductionmentioning

confidence: 97%

Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19

Nurdin,

Susanti,

Aidilof

et al. 2022

matrik

View full text Add to dashboard Cite

show abstract

A Mobile-Based Fuzzy Expert System for Diagnosing COVID-19

Mustapha,

Ogunbiyi,

Aleburu

et al. 2024

2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)

View full text Add to dashboard Cite

Revisión Sistemática de Literatura: Análisis de viabilidad para la detección y diagnóstico de Covid-19, aplicando modelos de Inteligencia Artificial (IA)

Jiménez

2021

CEDAMAZ

View full text Add to dashboard Cite

Desde la declaración de la emergencia sanitaria provocada por el Covid-19 en marzo del 2020, hasta la fecha, existen aproximadamente 219 millones de contagiados, de los cuales 4,5 millones han muerto. En nuestro país, se estima que existen 508 mil casos confirmados y aproximadamente 32 mil muertes a causa de esta enfermedad. Pese a disponer de métodos verificados para diagnosticar Covid-19, las pruebas Polymerase Chain Reaction (PCR) o Real Time-PCR (RT-PCR), tienden a generar falsos positivos y negativos entre el 30\% y el 40\%. Por tal razón, ayudar a los métodos tradicionales a realizar un diagnóstico clínico preciso, usando como datos de entrada radiografías pulmonares, supone un cambio radical en la detección de Covid-19, puesto que, es una alternativa mucho más cómoda para el paciente y lo que es más importante, aumenta el nivel de precisión reduciendo a la vez, las tasas de falsos positivos y negativos. En la presente Revisión Sistemática de Literatura (RSL), la cual se ha basado en la metodología de Bárbara Kitchenham, busca sustentar la creación de un modelo basado en la arquitectura de Redes Neuronales Convolucionales (CNN), capaz de analizar radiografías pulmonares para el diagnóstico de Covid-19. Como resultado, se pudo dar contestación a las tres preguntas de investigación planteadas, mismas que sirvieron para delimitar el presente estudio, para ello se analizó 41 trabajos relacionados (TR), los cuales se enfocaban en diferentes métodos de diagnóstico basados en Inteligencia Artificial (IA), no obstante 16 de estos TR hacían referencia al uso de CNN para el diagnóstico de Covid-19 mediante el análisis de tomografías computarizadas (TC) y radiografías pulmonares (Rayos X), siendo esta última la opción más viable para aplicarlo en nuestro entorno, debido la disponibilidad de datos. Además, el uso de recursos por parte de estos métodos es asequible tanto a nivel local, usando la Unidad de Procesamiento Gráfico (GPU) Nvidia y memoria RAM superior a 8GB como base, o utilizar procesamiento en la nube usando Google Colab.

show abstract

Expert System for Early Diagnosis of COVID - 19

Cited by 3 publications

References 0 publications

Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19

Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19

A Mobile-Based Fuzzy Expert System for Diagnosing COVID-19

Revisión Sistemática de Literatura: Análisis de viabilidad para la detección y diagnóstico de Covid-19, aplicando modelos de Inteligencia Artificial (IA)

Contact Info

Product

Resources

About