Enhanced Diagnostic Precision for Cardiovascular Diseases through the Synergistic Application of GDE_Lasso Feature Selection and Random Forest Classification Techniques
DOI:
https://doi.org/10.22399/ijcesen.736Keywords:
Gaussian based Differential Entropy, Information gain, BIC, Modified LASSO, Random ForestAbstract
Cardiovascular diseases (CVD) pose a significant global health challenge, contributing substantially to mortality rates worldwide. Early detection and diagnosis of CVD are critical, and machine learning techniques offer promising avenues for analyzing risk factors and implementing preventive measures. Feature selection methods can also help reduce diagnostic costs. Hence, in this work, Gaussian-based differential entropy for information gain with the Lasso (GDE_Lasso) feature selection model is proposed. The goal is to optimize diagnostics by streamlining processes, minimizing tests, and enabling targeted interventions. The proposed model is evaluated on Cleveland Datasets 1 and 2, respectively. This work compares the performance of Logistic Regression, Naïve Bayes, SVM, KNN, Decision Tree, XG Boost, and Random Forest for the considered datasets by applying the Z-score method. It was found that Random Forest performs well among the considered classifiers. Therefore, this study evaluates the performance of Random Forest with and without applying the GDE_Lasso feature selection algorithm.
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