Inference and Learning Methodology of Belief Rule Based Expert System to Assess Chikungunya

Sultana, Zinnia; Nahar, Lutfun; Basnin, Nanziba; Hossain, Mohammad Shahadat

doi:10.1007/978-3-030-82269-9_1

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…This greatly expands the range of applicability, as it is versatile and adaptable to a wide variety of medical conditions. This differs from other studies that focus on specific diseases such as malaria [11], COVID-19 [13], influenza [17], or chikungunya [15]. The developed system is notable for being lightweight and compatible with the Windows operating system.…”

Section: The Novelty Of the Study Compared To Previous Researchmentioning

confidence: 83%

“…Using six relevant symptoms and fuzzy sets implemented in a matrix laboratory (MATLAB), the system achieved an accuracy of 86.6% in tests with 15 cases of viral diseases, highlighting its usefulness in early diagnosis. In a similar approach, Sultana et al [15] addressed the early detection of the chikungunya virus (CHIKV) using a belief rule-based expert system (BRBES). She applied BRBES to real-world data and compared its performance with deep learning and machine learning models, demonstrating that BRBES is an efficient and fast option for assessing CHIKV in the early stages of infection.…”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent preliminary diagnosis system for diseases with similar clinical presentation

Andrade-Arenas,

Yactayo-Arias

2024

IJEECS

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Accurately identifying diseases with similar symptoms, especially in resource-limited medical settings, is a key challenge to diagnostic accuracy. This paper presents a preliminary diagnostic system to address the challenge of diagnosing diseases with similar symptoms. The system has been implemented using the PyQt5 library and employs a unique symptom identification algorithm developed in Python. Furthermore, to carry out the diagnosis, it uses comma-separated values (CSV) and excel files as databases where the diseases and their respective symptoms are stored. The results show that the system has a precision of 95%, a sensitivity of 90%, and a specificity of 93% after evaluating 35 clinical cases covering seven diseases with similar initial symptoms, namely: dengue, zika, chikungunya, COVID-19, influenza, monkey-pox, and the common cold. Furthermore, the positive evaluation of the technical performance of the system by experts supports its practical feasibility and its potential as a valuable tool in medical practice. In conclusion, the system diagnoses diseases by analyzing the symptoms of the information file, highlighting its usefulness in improving the diagnostic accuracy of similar cases and optimizing medical care for the benefit of patients.

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Section: The Novelty Of the Study Compared To Previous Researchmentioning

confidence: 83%

Section: Literature Reviewmentioning

confidence: 99%

Intelligent preliminary diagnosis system for diseases with similar clinical presentation

Andrade-Arenas,

Yactayo-Arias

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…Likewise, they propose a belief-based expert system (BRBES) to perform chikungunya virus diagnosis in patients in the initial stages of infection by the virus [19]. Receiver operating characteristic (ROC) curves have been utilized to infer the existence of chikungunya, and the idea is also based on real data.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of a Prototype Mobile Application Based on an Expert System for the Diagnosis of Diseases Transmitted by the Aedes Aegypti Mosquito

Jaúregui-Velarde,

Molina-Velarde,

Yactayo-Arias

et al. 2023

Int. J. Onl. Eng.

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The Aedes aegypti mosquito transmits the dengue, zika, and chikungunya viruses, endangering the health and lives of people in affected countries due to a lack of timely diagnosis. The objective of this study is to design and evaluate the feasibility of a mobile application based on an expert system for early diagnosis of diseases transmitted by the Aedes aegypti mosquito. The Buchanan methodology was used to develop the application. The results obtained show that the proposed mobile application has a diagnostic accuracy of 83%, a sensitivity of 91%, a specificity of 63%, and an error rate of 17%. The technical aspects of the application were also evaluated through a questionnaire administered to five computer experts. The results showed that the technical aspects of the application received an average rating of 3.91 out of a maximum of 5, with a standard deviation of 0.482. In addition, the usability of the application was evaluated using the standardized System Usability Scale (SUS), which was administered to a total of 15 users. The results of this evaluation showed that the application received an average score of 83 on the SUS scale, indicating a positive level of usability. In conclusion, the results support the effectiveness and potential of the application for the early diagnosis of diseases transmitted by the Aedes aegypti mosquito, providing a useful tool for the rapid detection of these diseases. Although it requires more attention to specificity and error rate to improve the accuracy of the diagnosis.

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Epilepsy Detection from EEG Data Using a Hybrid CNN-LSTM Model

et al. 2022

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Inference and Learning Methodology of Belief Rule Based Expert System to Assess Chikungunya

Cited by 8 publications

References 24 publications

Intelligent preliminary diagnosis system for diseases with similar clinical presentation

Intelligent preliminary diagnosis system for diseases with similar clinical presentation

Evaluation of a Prototype Mobile Application Based on an Expert System for the Diagnosis of Diseases Transmitted by the Aedes Aegypti Mosquito

Epilepsy Detection from EEG Data Using a Hybrid CNN-LSTM Model

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