Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 5 , Issue 1 , PP: 29-37, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Doaa Sami Khafaga 1 * , Abdelhameed Ibrahim 2 , S. K. Towfek 3 , Nima Khodadadi 4
Doi: https://doi.org/10.54216/JAIM.050103
Due to its potential to enhance patient outcomes and ease individualized therapy, predictive medicine has received considerable interest in recent years. In this article we examine the use of data mining in predictive medicine, with a particular emphasis on hemodynamic prediction for abdominal aortic aneurysm (AAA) disease. In AAA, the abdominal aortic wall becomes weakened and may rupture, putting the patient's life in danger. Clinical decision making and treatment planning for AAA rely heavily on accurate hemodynamic prediction. For developing these predictive models for hemodynamic assessment, we use the well-known data mining techniques of Random Forest (RF) and AdaBoost. To capture complicated interactions, the RF approach employs a collection of decision trees, while AdaBoost iteratively improves the model by giving more weight to examples that were incorrectly classified. The experimental evidence shows that these methods are effective in providing reliable estimates of the hemodynamics of AAA. This research adds to the expanding field of predictive medicine by providing new understanding of the potential of data mining methods to improve the quality of care for patients with AAA illness.
Predictive medicine , Data mining , Hemodynamic prediction , Abdominal aortic aneurysm (AAA) , Random Forest , AdaBoost.
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