Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Laberiano Andrade-Arenas
Universidad de Ciencias y Humanidades
Computer Science & IT Electrical & Electronics Engineering Engineering

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Intelligent preliminary diagnosis system for diseases with similar clinical presentation Laberiano Andrade-Arenas; Cesar Yactayo-Arias
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1131-1143

Abstract

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.
Mobile design for medical care and minor emergencies applying telemedicine Misael Lazo-Amado; Laberiano Andrade-Arenas
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1889-1902

Abstract

The large number of patients today in hospitals or clinics shows a total collapse in the medical areas, this is reflected by the lack of available specialists and the saturation of appointments to attend patients, taking minutes and hours to get to be attended, that is why it is proposed as an objective to design a mobile application for medical care and minor emergencies applying telemedicine offering the main processes of appointment registration, video calls, medical qualification, first aid, prescription management, and emergency calls. This mobile application is made with the Design Thinking methodology that will allow the team to find the main problems or risks of the patient and find a quick solution based on design. The results give response to the 150 patients showing their satisfaction of the proposed solution, also validation by experts is performed indicating a 97% acceptance and specialist doctors indicate satisfaction, accepting that the system is efficient. In conclusion, the applied objective is shown, finding the main problems and looking for quick solutions showing efficiency and satisfaction by patients, experts, and medical specialists.
Design of an educational platform based on augmented reality for the special education of children with dyscalculia Jhair Baltodano-Payahua; Sebastian Ramos-Cosi; Alicia Alva-Mantari; Gustavo Villar-Mayuntupa; Enrique Lee Huamani; Meyluz Paico-Campos; Laberiano Andrade-Arenas
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10754

Abstract

Dyscalculia is a learning disorder that affects between 3% and 8% of the world's population and is characterized by difficulty in acquiring basic mathematical concepts, mainly in children. In view of this, several technological tools have been explored that can help improve the education of these children, with augmented reality (AR) being a promising alternative. The objective of this research is to design an AR-based platform to support the special education of children with dyscalculia. An agile methodology, Scrum, was used for the development, which allowed for an interactive approach to the design of the prototypes. Consequently, these were evaluated and validated by six experts in the field. The result was an acceptance rate of 81.2%, demonstrating the viability of the prototype. It is concluded that the prototype using AR is an effective tool to support children with dyscalculia aged 8 to 10, as it allows for an interactive and beneficial learning experience to nurture their knowledge.
Predicting university student dropouts in Latin America using machine learning Laberiano Andrade-Arenas; Inoc Rubio Paucar; Margarita Giraldo Retuerto; Cesar Yactayo-Arias
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp628-641

Abstract

In the university context, student dropout has become one of the most recurring problems, both in the short and long term. The objective of this research was to develop a predictive model using the random forest (RF) algorithm to identify patterns associated with university dropout. To achieve this, the knowledge discovery in databases (KDD) methodology was applied, which encompasses the stages of selection, preprocessing, transformation, data mining, and interpretation of results. The RF model demonstrated superior performance compared to other evaluated models, achieving an accuracy of 87%, a precision of 86%, a recall of 85%, an F1-score of 85%, and an receiver operating characteristic (ROC) area under the curve (AUC) of 0.91, highlighting its high predictive capability compared to other techniques analyzed. Therefore, the application of the proposed model is recommended in various university institutions in order to identify potential dropout cases at an early stage.
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.
A critical review of the state of computer security in the health sector Raúl Jáuregui-Velarde; Domingo Hernández Celis; Cesar Yactayo Arias; Laberiano Andrade-Arenas
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5394

Abstract

There is growing concern about IT security in the healthcare sector due to the number of cyberattacks. The objective of the review is to analyze the state of adoption of computer security in the healthcare sector and provide valuable knowledge to researchers and health organizations interested in this field of study. An exhaustive search of international and regional articles on computer security in healthcare organizations was conducted using Scopus, Dimensions, and pubMed databases. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement was used for the selection of articles published between 2018 and 2022. The final number of articles considered is 50. The review explored approaches related to computer security types, mechanisms, and technologies. The findings reveal that blockchain is the most widely used technology to protect medical information. In addition, network, software, and hardware security approaches are employed, using mechanisms such as data encryption, authentication, and access control. Based on these findings, a perimeter security model for the protection of medical information is proposed. In conclusion, these results highlight the importance of adopting robust security measures in terms of networks, software, and hardware, as well as adopting blockchain technology to improve data security in the healthcare sector.
Co-Authors Alejandro Boza-Chua Alexi Delgado Alicia Alva-Mantari Avid Roman-Gonzalez Carlos López Carlos Manuel Oscco Aguero Cesar Augusto Yactayo Arias Cesar Yactayo Arias Cesar Yactayo-Arias David Llulluy Nuñez Domingo Hernández Celis Elizabeth Arelli Liñan Espinoza Elizabeth Liñan Espinoza Enrique Lee Huamani Erich Jordy Echevarría Carpio Gustavo Villar-Mayuntupa Inoc Rubio Paucar Inoc Rubio Paucar Jhair Baltodano-Payahua Jimmy Cruz Fernandez Margarita Giraldo Retuerto Mario Chungas Meyluz Paico-Campos Misael Lazo-Amado Natalia I. Vargas-Cuentas Noe Jacinto Raúl Jáuregui-Velarde Roberto Demmis Muñoz Villacorta Sebastian Ramos-Cosi William Wilfredo Reyes Perez