Artificial Intelligence-Based Mobile Application for Predicting Asthma Attacks

Estrada-Leon L.R.; Cieza-Mostacero S.E. · TEM Journal (2026) · DOI: 10.18421/tem153-08

Resumen

– This study presents applied research using a pure experimental design, with the objective of improving the prediction process of asthma attacks through a mobile application based on Machine Learning. For this purpose, software development tools such as Flutter, FastAPI, Render and Firebase were used. The results indicate an increase in the Number of New Cases to 98 cases, along with a decrease in the Time to Detection to 17 milliseconds in the experimental group compared to the control group. In addition, an 80% increase in the detection accuracy was observed. The comparison between both groups led to the acceptance of the alternative hypotheses, statistically demonstrating the effectiveness of the machine learning-based mobile application in improving the asthma attack prediction process. In conclusion, the remarkable improvements in the prediction of asthmatic attacks are highlighted, increasing both the number of new cases and detection accuracy while also reducing the detection time.

Instituciones: Universidad César Vallejo