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Our final design consist of a cascade system to take care of highly imbalanced information. In the 1st stage, a binary model is in charge of forecasting whether an individual has a decreased chance of developing CVDs or if perhaps has a risk that really needs interest. In this step, we utilize six formulas logistic regression, SVM, random woodland, XGBoost, CatBoost, and multilayer perceptron. The higher outcomes delivered an average reliability of 0.86 ± 0.03 and f-score of 0.85 ± 0
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