Chikungunya diagnosis using artificial intelligence and medical record data
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1372Keywords:
Artificial intelligence, Chikungunya fever, Clinical diagnosisAbstract
Objective: The purpose of this research is to develop and evaluate a machine learning model to assist in the clinical diagnosis of chikungunya fever using patient medical records. Method: Data were obtained from the Open Data Portal of Recife City Hall, encompassing 18,881 patient records. Results: After preprocessing and cross-validation, the Random Forest model with 100 trees showed the best performance, with an accuracy of 93.40% and a receiver-operator characteristic area of 0.990. The model application demonstrated high efficacy in differentiating between chikungunya and other conditions. Conclusion: We conclude that the use of artificial intelligence can significantly improve the clinical diagnosis of arboviruses. Future work includes expanding the database, integrating the model into clinical environments, and exploring advanced machine learning techniques.
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