Prediction models applied in stroke diagnosis: a scope review
DOI:
https://doi.org/10.59681/2175-4411.v15.i2.2023.980Keywords:
CVA, prediction models, scope reviewAbstract
Objective: In this article, a scope review is presented with the objective of identifying prediction models applied in the diagnosis of Cerebral Vascular Accident (CVA). Method: RE was performed on five search sources, using a search string and inclusion and exclusion criteria. Results: After carrying out the steps defined in the protocol, 615 papers were returned in the first step, of which only 9 were selected to be analyzed and have their information extracted. Conclusion: Through the results presented, it was possible to identify that most of the works developed learning models, followed by the comparison of algorithms and creation of algorithms. Regarding the resources used, the most used were: Python programming language and scikit-learn library. The most used models and algorithms are: Decision tree, Naive Bayes, Random Forest and KNN (K-Nearest Neighbors). Most of the works analyzed used the metrics Recall, Precision, F1-Score and Accuracy to validate the solutions. Among the identified limitations, those related to the evaluation of the performance of the proposed solutions and the absence of relevant aspects for the analyzed studies stand out.
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