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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Cabanillas-Carbonell, Michael
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Computer Science & IT Electrical & Electronics Engineering Engineering Health Professions

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Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2 Garcia-Rios, Victor; Marres-Salhuana, Marieta; Sierra-Liñan, Fernando; Cabanillas-Carbonell, Michael
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1713-1726

Abstract

Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus.
Mobile application for the control process of childhood anemia in time of the pandemic Mancisidor-Bazán, Leonel; Morales-Guillén, Isaac; Cabanillas-Carbonell, Michael
International Journal of Public Health Science (IJPHS) Vol 12, No 4: December 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i4.23143

Abstract

The objective of the research was to implement a mobile application for the control process of childhood anemia in times of pandemic, in order to have better medical control with faster care, better control of the diagnosis of anemia in children, and better performance and satisfaction. The following Scrum method was applied. The research has a quantitative approach, of experimental type. The sample consisted of 40 children. Results were obtained regarding time control in the speed of care, an increase of 10.2% was obtained; regarding the number of diagnoses, control, and follow-up in recovery there was an increase of 33.3% and finally regarding the control of performance and satisfaction with care there was an increase of 73.3%.
Risk analysis and prevention in computer security in institutional servers, a systematic review of the literature Namo-Ochoa, Angel; Portilla-Cosar, Eduardo; Sierra-Liñan, Fernando; Cabanillas-Carbonell, Michael
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6093

Abstract

In recent years, computer attacks on the server infrastructure in organizations have been increasing, and the pandemic of covid-19 and remote work have been the main causes for this massive wave of large-scale attacks, small businesses are especially vulnerable because to optimizing resources they leave aside the cyber security in their network infrastructure. The present research is a systematic review that compiles 58 articles where policies, techniques, and infrastructure for the prevention of threats in enterprise servers have been implemented and raised, these articles have been collected from major databases such as IEEE Xplore, SAGE, Science Direct, Scopus, and IOP Publishing. The results show that one of the most effective methods in preventing communications between institutional servers is public key infrastructure/SSL-TLS encryption. Most research claims that it is the most effective method as it provides a central certifier and manages the certificates for the servers allowing each of the modules or attachments within the infrastructure to identify and validate other members and to proceed with the encryption of network traffic, Finally, a security implementation model is proposed.
Performance analysis of 10 machine learning models in lung cancer prediction Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1352-1364

Abstract

Lung cancer is one of the diseases with the highest incidence and mortality in the world. Machine learning (ML) models can play an important role in the early detection of this disease. This study aims to identify the ML algorithm that has the best performance in predicting lung cancer. The algorithms that were contrasted were logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), gaussian Naive Bayes (GNB), multinomial Naive Bayes (MNB), support vector classifier (SVC), random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and gradient boosting (GB). The dataset used was provided by Kaggle, with a total of 309 records and 16 attributes. The study was developed in several phases, such as the description of the ML models and the analysis of the dataset. In addition, the contrast of the models was performed under the metrics of specificity, sensitivity, F1 count, accuracy, and precision. The results showed that the SVC, RF, MLP, and GB models obtained the best performance metrics, achieving 98% accuracy, 98% precision, and 98% sensitivity.
Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp222-237

Abstract

Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.
Seeking best performance: a comparative evaluation of machine learning models in the prediction of hepatitis C Cabanillas-Carbonell, Michael; Zapata-Paulini, Joselyn
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp374-386

Abstract

Hepatitis C is a disease that affects millions of people worldwide. It is spread through contact with contaminated blood through injections, transfusions, or other means. It is estimated that with early detection patients have a higher rate of recovery. The objective of this study is to perform a comparative evaluation of different models focused on the prediction of hepatitis C, to determine which of the models offers better performance in accuracy, precision, and sensitivity. The models used were logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), and gradient boosting (GB), aimed at hepatitis C prediction. The training of the models was carried out using a dataset composed of 615 records, which incorporate 14 attributes. The structure of the article is divided into six sections, including introduction, review of related articles, methodology, results, discussion, and conclusions. The performance of the models was evaluated through metrics such as accuracy, sensitivity, F1 count, and, mainly, precision. The results obtained place the DT model as the most efficient predictor, reaching a precision, accuracy, sensitivity, and F1-score of 95%.
Mobile application to optimize appointment management in a specialized dental center Urbina-Novoa, Joel; Cabanillas-Carbonell, Michael
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9175

Abstract

The objective of the research was to implement a mobile application for the management of appointments in a specialized dental center, to improve patient care, allowing them to make their reservation from the place and at the time they want. The research has a quantitative approach, of experimental type with a pre-experimental design. The population consisted of 70 patients, with a total sample of 60. The SPSS statistical software was used for the elaboration of the results, obtaining positive results. With all the above mentioned in this research work, it is concluded that the implementation of the mobile application for appointment management for the dental center will facilitate patients to have better attention, which allows a reduction of time and satisfaction with the service. In indicator 1, referring to appointment registration time, a reduction of 44.13% was obtained. In indicator 2 on the number of patients presenting for appointments, an increase of 17.58% was obtained, and finally, in indicator 3 on the level of satisfaction, an increase of 65% was obtained.
Artificial intelligence applications in agriculture: a systematic review of literature Cabanillas-Carbonell, Michael; Zapata-Paulini, Joselyn
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3503-3519

Abstract

Artificial intelligence (AI) is transforming agriculture by offering innovative solutions to persistent challenges. This systematic literature review explores the most studied AI applications in agriculture, emphasizing crop management, agronomic decision-making, early detection of diseases and pests, and climate change adaptation. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 700 publications were retrieved from databases such as Scopus, ScienceDirect, and IEEE Xplore, with 104 relevant articles selected after applying strict inclusion and exclusion criteria. The findings underscore the importance of machine learning and image processing in tailoring agronomic practices to specific plot conditions and microclimates. These tools enable early identification and control of plant diseases and pests, reducing crop losses and dependence on chemicals. Nonetheless, challenges remain, particularly regarding accessibility for smallholder farmers, high implementation costs, and limited data infrastructure. While AI offers significant potential to enhance agricultural productivity, sustainability, and resilience, addressing these limitations is crucial. A balanced, inclusive approach is essential to ensure AI’s benefits are widely distributed and contribute to long-term food security and environmental sustainability.
The contribution of artificial intelligence in people with autism: a systematic literature review Moza-Villalobos, Anderson; Cabanillas-Carbonell, Michael
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4442-4453

Abstract

Autism is a disorder that poses significant challenges in various areas such as health, education, social interaction, and how the world perceives them. The implementation of artificial intelligence in daily life and different fields offers an innovative approach to addressing these challenges, facilitating early detection, support in learning, and social interaction for individuals with this condition. The systematic literature review focuses on studying 50 out of 144 articles obtained from various databases such as EBSCO Host, IEEE Xplore, ScienceDirect, Scopus, ProQuest, and Web of Science. These articles were systematically organized using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, providing information about machine learning as the most utilized discipline, the types of infrastructure it relies on, and the countries that are at the forefront of this topic. This review will serve as a reference for stakeholders regarding the advancements and contributions of artificial intelligence for individuals with autism.
Classification algorithm with artificial intelligence for the diagnostic process of obstructive sleep apnea Ventura-Tecco, Jehil; Fajardo-Avalos, Jesús; Cabanillas-Carbonell, Michael
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4520-4532

Abstract

Obstructive sleep apnea (OSA) is a disease that affects millions of people worldwide, and a large proportion of them remain undiagnosed due to the high cost of polysomnography (PSG) tests. For this reason, it is crucial to develop affordable diagnostic tools to facilitate early detection of this condition. This study aims to analyze how an artificial intelligence (AI) based classification algorithm impacts the diagnostic process of OSA in Lima, Peru. The algorithm was developed following the Kanban methodology, which guaranteed an efficient and transparent follow-up during the development cycle, which is key in the medical context where software quality and traceability are fundamental. A decision tree (DT) was used for diagnosis and classification, employing a training dataset provided by the National Sleep Research Resource (NSRR), from which six relevant attributes were selected for analysis. The research results indicated that, although the improvement in clinical diagnostic accuracy was minimal at 10.81%, positive results were obtained in other aspects: diagnostic time was significantly reduced by 28.17%, and the number of tests required decreased by 24.07%.
Co-Authors Fajardo-Avalos, Jesús Garcia-Rios, Victor Mancisidor-Bazán, Leonel Marres-Salhuana, Marieta Morales-Guillén, Isaac Moza-Villalobos, Anderson Namo-Ochoa, Angel Portilla-Cosar, Eduardo Sierra-Liñan, Fernando Urbina-Novoa, Joel Ventura-Tecco, Jehil Zapata-Paulini, Joselyn