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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sierra-Liñan, Fernando
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Computer Science & IT Engineering

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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.
Predicting hepatitis C infection with machine learning algorithms: a prospective study Iparraguirre-Villanueva, Orlando; Ornella Flores-Castañeda, Rosalynn; Chero-Valdivieso, Henry; Sierra-Liñan, Fernando
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4403-4413

Abstract

Globally, chronic hepatitis C virus (HCV) infection affects millions of people and leads to a high number of deaths annually. In 2019, the World Health Organization (WHO) recorded around 290,000 deaths related to HCV, a virus transmitted mainly through blood that causes liver damage. The virus has infected more than 169 million people worldwide. This study aims to compare the performance of machine learning (ML) models for HCV detection. ML models such as logistic regression (LR), random forest (RF), decision tree (DT), and catBoost classifier (CATBC) were used. To carry out this task, a dataset of 615 patient records, and 14 variables were used. This research process was carried out in multiple phases, encompassing model understanding, data analysis and cleaning, ML model training, and subsequent model evaluation. The results revealed that the gradient boosting (GB) model stood out by achieving the best performance and highest accuracy, achieving a rate of 94% in HCV detection, this demonstrates outstanding performance compared to the other models such as LR, RF, k-nearest neighbor (KNN), DT, and CATBC, which obtained accuracy rates of 89%, 93%, 85%, 93%, 93%, and 92%, respectively. It can be concluded that the GB model stands out as the best algorithm for this task.
Co-Authors Cabanillas-Carbonell, Michael Chero-Valdivieso, Henry Garcia-Rios, Victor Iparraguirre-Villanueva, Orlando Marres-Salhuana, Marieta Ornella Flores-Castañeda, Rosalynn