Sistema de suporte à decisão baseado em inteligência artificial para predição de doenças arteriais coronárias

Abstract

The application of machine learning has become increasingly common in various professional areas. Among the various possibilities that this technology allows, using it as a prediction and decision-making tool has been very promising. However, the "Black-Box" feature of some models has made the use of this technology unviable, especially in the medical field. Healthcare professionals need clarity about the factors that indicate a diagnosis. After all, a wrong diagnosis can lead to a patient’s life. Using a database with more than 560,000 records of medical appoint ments in patients, this work proposes a methodology that builds explainable machine learning models for the diagnosis prediction of auricular fibrillation, coronary sickness, and sleep apnea, incorporating patient’s structured and unstructured historical data. Weak supervision is used to label the unstructured data, XGBoost is used for prediction, and the SHAP method is used to explain the prediction. Finally, the methodology is implemented in Web Software written in the Python programming language. The results are promising, in addition to the accurate prediction capability, the prediction explanation highlights the patient’s historical characteristics with a higher impact on the decision-making process of the suggested diagnostic.