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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Cesar Yactayo-Arias
Universidad Continental
Computer Science & IT 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.
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.
Co-Authors Inoc Rubio Paucar Laberiano Andrade-Arenas Margarita Giraldo Retuerto