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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Yactayo-Arias, Cesar
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Prototype design of a mobile app oriented to adults with obesity Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6745-6753

Abstract

Obesity in adults is a worldwide problem, which is why different countries, through their health-related agencies, implement policies to fight this disease. One of the tools is the use of a mobile application that controls obesity. In this sense, the prototype was designed taking into account different items such as physical activities, body mass index, calorie intake, and food options, among others. The objective of the research is to design a mobile app that allows us to control of obesity in adults. The methodology used is design thinking which allows us to use a systematic approach to reach the objective. An interview was conducted to identify the needs of the user and obtain information regarding their essential needs. In addition, a survey was carried out, the outcome shows satisfaction with a 58% acceptance rate. The beneficiaries of this research are adults who suffer from obesity and healthcare centers. Likewise, research has a positive impact since it focuses on solving problems directly related to health issues.
Expert system for diagnosing learning disorders in children Andrade-Arenas, Laberiano; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2965-2975

Abstract

Given the urgent need for early detection of learning disorders such as dysgraphia, dyslexia, and dyscalculia in children, this study aimed to evaluate an expert system developed in Python to facilitate early diagnosis of these disorders. The background highlights the importance of providing parents, educators, and health professionals with an effective tool for early detection of these disorders. In 21 simulated cases, the system showed impressive performance with an accuracy rate of 95%, a precision of 100%, a sensitivity of 93%, and a specificity of 100%. Furthermore, the acceptability evaluation, conducted with 15 parents selected by convenience sampling, showed a high level of satisfaction, with an overall mean of 4.78 and a standard deviation of 0.45, indicating consistency in responses. In conclusion, this study confirms the effectiveness of the expert system in the early diagnosis of learning disabilities, providing parents, educators, and health professionals with a valuable tool. Despite these encouraging results, the need for additional research is recognized to address limitations and improve the external validity of the system to ensure its widespread utility and adaptability in real clinical settings.
A bibliometric analysis of the advance of artificial intelligence in medicine Andrade-Arenas, Laberiano; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3350-3361

Abstract

This bibliometric study analyzes the evolution of research in artificial intelligence (AI) applied to medicine from 2015 to September 2023. Using the Scopus database and keywords related to AI, machine learning, and deep learning in medicine, tools such as VOSviewer and Bibliometrix were used to explore publication trends, subject areas, co-authorship networks, and the most productive countries, among others. 2,064 articles were analyzed, and a significant increase in global academic production has been evident in the last five years. International collaboration was notable, with China and the United States leading in knowledge contribution. The keyword analysis highlights the breadth of topics and applications of AI in medicine, with particular emphasis on cancer detection, dengue diagnosis, and medical image analysis, among others. In conclusion, this study highlights the growing academic interest in the application of AI in medicine and the need for collaborative research. The findings underscore the relevance of these technologies in key areas of health care, contributing significantly to advances in medical diagnosis and prognosis.
Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector Jáuregui-Velarde, Raúl; Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1079-1090

Abstract

This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.
A study of Tobacco use and mortality by data mining Arenas, Laberiano Andrade; Paucar, Inoc Rubio; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6861-6873

Abstract

The use of data mining to address the issue of people who consume tobacco and other harmful substances for their health has led to a significant dependence among smokers, which over time causes illnesses that may result in the addict's death. As a result, the research's goal is to apply a data mining study whose findings showed that the confidence intervals are less than 0.355. However, the lift and conviction in the last three rules are also lower, making it unlikely that these rules will be followed. On the other hand, the knowledge discovery in data bases method was used. It consists of the following stages: data selection, preparation, data mining, and evaluation and interpretation of the results. To that end, comparisons of agile data mining methodologies like crisp-dm, knowledge discovery in data, and Semma are also done. As a result, using specific criteria, dimensions are segmented to allow for the differentiation of these methodologies. As a result, a comparison graph of models such as naive Bayes, decision trees, and rule induction is used. To sum up, it can be said that the rules of association apply to men, the number of admissions, and the cancers that can be brought on by smoking. Also, the percentage of male patients admitted with cancers that can be brought on by smoking Last but not least, the number of admissions and cancers that can be brought on by smoking
Mobile application for the prevention and self-care of varicose veins Andrade-Arenas, Laberiano; Retuerto, Margarita Giraldo; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6560-6571

Abstract

Details the process of creating a prototype of a mobile application designed to promote prevention and self-care of varicose veins in patients at high vascular risk. The objective is to investigate the experience of patients at high vascular risk when using a mobile application created for the prevention and self-care of varicose veins. The methodology used is design thinking, a user-centered approach that seeks to solve complex challenges through creativity, design and problem solving. The results obtained from the expert judgment, based on ATLAS.ti 23, provide valuable insight into the feasibility and potential of the technological tools as the interface has the highest variability among the criteria evaluated, followed by interaction and quality, while usability presents the lowest variability. This suggests that usability evaluations tend to be more consistent compared to the other criteria. In conclusion, the present work analyzes how mobile applications can play a crucial role in promoting prevention and self-care of varicose veins in patients at high vascular risk. The good reception of the prototype confirms the importance of technology in the field of vascular health and highlights the value of this approach to improve quality of life and health management in this demographic group.
Predictive models in Alzheimer's disease: an evaluation based on data mining techniques Andrade-Arenas, Laberiano; Rubio-Paucar, Inoc; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2988-3002

Abstract

The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.
Data mining for predictive analysis in gynecology: a focus on cervical health Andrade-Arenas, Laberiano; Rubio-Paucar, Inoc; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2822-2833

Abstract

Currently, data mining based on the application of detection of important patterns that allow making decisions according to cervical cancer is a problem that affects women from the age of 24 years and older. For this purpose, the Rapid Miner Studio tool was used for data analysis according to age. To perform this analysis, the knowledge discovery in databases (KDD) methodology was used according to the stages that this methodology follows, such as data selection, data preparation, data mining and evaluation and interpretation. On the other hand, the comparison of methodologies such as the standard intersectoral process for data mining (Crips-dm), KDD and sample, explore, modify, model, evaluate (Semma) is shown, which is separated by dimensions and in each dimension both methodologies are compared. In that sense, a graph was created comparing algorithmic models such as naive Bayes, decision tree, and rule induction. It is concluded that the most outstanding result was -1.424 located in cluster 4 in the attribute result date.
Preliminary diagnosis of respiratory diseases: an innovative approach using a web expert system Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6600-6611

Abstract

This study addressed the challenge of accurate and timely diagnosis of respiratory diseases such as influenza, asthma, and pneumonia by developing and evaluating a web-based expert system. The objective was to develop and assess both the usability and diagnostic efficiency of a web- based expert system adaptable to mobile devices. A combined methodological approach was used, using the rapid application development (RAD) model to build the system and the user usability system (SUS) to evaluate the usability with the participation of 15 users and 21 simulated cases with a confusion matrix to determine the precision, accuracy, sensitivity, and specificity of the system in diagnosing respiratory diseases. The results showed that the expert system has a considerable capacity to identify and differentiate these diseases, with a precision of 86%, an accuracy of 76%, a sensitivity of 80%, and a specificity of 67%. Furthermore, the usability evaluation using the SUS method yielded an average of 82, indicating a positive perception and good usability by the users. In conclusion, although the results suggest a promising potential to improve the diagnostic process in clinical and community settings, the need for future studies to validate its performance in real clinical settings is recognized.
Co-Authors Andrade Arenas, Laberiano Andrade-Arenas, Laberiano Arenas, Laberiano Andrade Celis, Domingo Hernández Giraldo Retuerto, Margarita Giraldo-Retuerto, Margarita Hernández Celis, Domingo Jáuregui-Velarde, Raúl Molina-Velarde, Pedro Paucar, Inoc Rubio Pucuhuayla-Revatta, Félix Retuerto, Margarita Giraldo Rubio-Paucar, Inoc