Following the advances in measurement technology and its vast availability, mechanical systems and structures are increasingly equipped with sensors to obtain continuous information regarding the system state. Coupled with robust numerical models, this information can be used to build a numerical twin of the structure that is linked to its physical twin via a feedback loop. This results in the concept of Dynamic Data Driven Application Systems (DDDAS) that can predict and control the evolution of the physical phenomena at stake on the structure, as well as dynamically updating the numerical model with the help of real-time measurements [1,2].
Cardiovascular diseases are one of the most common diseases and currently, the number of people with cardiovascular diseases is increasing. However, if necessary treatment is not provided for the patient at the right time, it might lead to patient death. Therefore, accurate diagnosis
of cardiac problems during the first examination along with suitable treatment can decrease the rate of mortality due to cardiovascular diseases. To this end, data mining techniques can be used. Data mining extracts the necessary data from a large body of information. This data is then is
used for data classification and prediction through clustering, classification and/or identification of hidden patterns. Many studies so far have focused on using data mining techniques to diagnose cardiovascular diseases. The present study aims to provide a diagnostic model for cardiovascular
diseases using an approach based on feature selection and data clustering as pre-processing steps. The proposed model involves 4 main phases: (1) Pre-processing the data to eliminate null and outlier values from data sets; (2) Choosing effective features by using three methods of Pearson correlation
coefficient, Information Gain algorithm, and analysis of the main components which try to remove the features that do not have a special relationship with target feature and the behavior of this feature is independent of the target feature; at the end of this phase, 5 features of 13 initial
features are removed. (3) Using the KMeans algorithm in data clustering and developing pre-processes before creating the final cluster and developing a model for predicting the type of cardiovascular diseases. The results obtained from the proposed solution show that am4 algorithms
of ID3, Naïve Bayes, SVM, and IBK used, IBK algorithm was the most accurate algorithm with 0.97 accuracy.
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