The Use of Data Mining Techniques in the Diagnosis and Prevention of Cerebrovascular Accident (CVA)
DOI:
https://doi.org/10.5753/jidm.2024.3053Keywords:
Stroke, Risk factors, Indicative signs, Data miningAbstract
Over the years, there has been a rise in the occurrence of Cerebrovascular Accident (CVA) cases, due to the increase in the elderly population. Current data indicate that stroke is one of the leading causes of death and disability worldwide, affecting millions of people and leaving survivors with numerous sequelae, whether they are physical or mental. Many factors such as diabetes, smoking, high blood pressure, and others, favor the onset of stroke, which increases mortality rates, making it necessary to know these factors in order to contribute to early preventive measures. In this sense, the purpose of this article is to use six data mining algorithms with the objective of helping to identify and diagnose people prone to having a stroke based on risk factors and indicative signs. The algorithms used were: Decision Tree, K-Nearest Neighbors (K-NN), Multilayer Perceptron Neural Network (MLP), Support Vector Machine (SVM), Naive Bayes, and the Apriori algorithm. The results showed that the MLP and decision tree algorithms obtained the best results, indicating their use in intelligent solutions for this area.
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