Abstract
Data mining is a powerful tool applied to various domains such as the e-health field. For instance, the prediction of cardiovascular diseases is a sub-field where Data Mining has contributed to automate the diagnosis and sometimes may be applied in the treatment stage of the disease. This paper aims to define the efficient classifier medical decision support system compared to four classification algorithms (K-Nearest Neighbors, Support Vector Machines, Multilayer Perceptron, and the decision tree algorithm C4.5). Those single techniques were combined for better results. Indeed, it was found that ensemble learning (stacking and bagging) is the chosen method that returns better results based on the training on two types of datasets. In addition, it is well known that blood pressure is one of the significant factors in cardiovascular diseases as well as the IMC for obesity, among others. The purpose is to specify the level of the factors that we dispose in our database that impacts the most the prediction. Data are processed for systolic blood pressure. It was found that systolic blood pressure above 129.5 mm Hg is highly impacting the development of a cardiovascular event. Under this value, other attributes are considered such as age and cholesterol LDL level.
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Douifir, K., Benabdellah, N.C. (2023). The Impact of Systolic Blood Pressure Level and Comparative Study for Predicting Cardiovascular Diseases. In: Masrour, T., Ramchoun, H., Hajji, T., Hosni, M. (eds) Artificial Intelligence and Industrial Applications. A2IA 2023. Lecture Notes in Networks and Systems, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-031-43520-1_10
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DOI: https://doi.org/10.1007/978-3-031-43520-1_10
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