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

Abstract

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.

Expert system for diagnosing learning disorders in children

“…The methodology used for the development of the diagnostic system for learning disabilities in children is based on the Buchanan methodology, which follows a conventional waterfall life cycle approach divided into five stages, as illustrated in Figure 1, namely identification, conceptualization, formalization, implementation, and test [20]. In the identification phase, the problem is thoroughly analyzed, the system requirements are established, and the solution to be implemented is defined.…”

Intelligent preliminary diagnosis system for diseases with similar clinical presentation

“…As sensitivity increases, the likelihood of false negatives decreases. This parameter can be calculated using (2) and provides an essential quantitative measure for evaluating the performance of the diagnostic system.…”

“…Some diseases have a wide range of clinical manifestations, increasing the likelihood that two or more conditions will coexist [1]. For example, diseases caused by dengue, chikungunya, zika, and yellow fever viruses are closely linked to the behavior and development of the aedes aegypti mosquito [2]. Due to similarities in their clinical and epidemiological behavior, these diseases are often misdiagnosed [3], posing a serious threat to public health in tropical and subtropical areas [4]- [6].…”

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

“…After having successfully identified and understood the nature of the problem, we proceeded to the formalization phase. During this stage, the main focus was on knowledge creation and practical operation of the expert system [25]. Table 3 presents the formalization of knowledge, which will be fundamental to carry out the implementation and operation of the system.…”