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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Inoc Rubio Paucar
Universidad Privada Norbert Wiener
Computer Science & IT Electrical & Electronics Engineering Engineering

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Mobile application: expert systems model for disease prevention Inoc Rubio Paucar; Sttaly Ascona Rivas; Laberiano Andrade-Arenas; Domingo Hernandez Celis; Michael Cabanillas-Carbonell
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5224

Abstract

In recent years, both locally and globally, many citizens are cornered by different diseases which grates a lot of concern in the person, due to the collapse of different medical centers, it is necessary to use information systems. The objective of the research is to develop a mobile application that allows detecting what type of disease a patient suffers from and maintaining communication with the expert in the field using an expert system such as azure machine learning studio that allows detecting the deadliest diseases. For the development of this research, the rup methodology was applied, which allows the use of different techniques where the necessary activities can be carried out with efficient communication. For the validation of this project, a survey was used for the experts with a questionnaire of questions, giving a positive result in the implementation of this project. The result was an acceptance of 83.3% in a high way in their survey responses. In conclusion, this mobile application was successfully designed, benefiting many people and, above all, preventing dangerous diseases that can even lead to death.
Predictive model based on machine learning to identify sleep-related health problems Laberiano Andrade-Arenas; Inoc Rubio Paucar; Margarita Giraldo Retuerto; Cesar Yactayo-Arias
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3888-3902

Abstract

Sleep quality has become a growing public health issue worldwide, mainly due to a lack of awareness about its long-term consequences. Despite existing strategies to address this problem, there remains a need for more effective approaches. In this study, an early detection model for sleep disorders was implemented using the extreme gradient boosting (XGBoost) algorithm, following the knowledge discovery in databases (KDD) methodology, which includes the phases of selection, preprocessing, transformation, data mining, and interpretation. A dataset extracted from the Kaggle platform in CSV format was used, consisting of 374 records. With an overall accuracy of 91.5%, a recall of 100% for the insomnia class, and a precision of 100% for sleep apnea, the proposed model demonstrated exceptional performance. It also received an area under the curve (AUC) of 0.909 and an average F1-score of 0.913. With a mean accuracy of 91%, a 95% confidence interval (0.89–0.94), and a p-value of 0.0012, cross-validation confirmed its robustness and showed a statistically significant change from the baseline model. The error rates remained within clinically acceptable ranges, confirming its applicability as a diagnostic support tool. Overall, the results demonstrate the effectiveness of the model in identifying patterns related to sleep disorders.
Co-Authors Cesar Yactayo-Arias Domingo Hernández Celis Laberiano Andrade-Arenas Laberiano Andrade-Arenas Margarita Giraldo Retuerto Michael Cabanillas-Carbonell Sttaly Ascona Rivas