Analyzing Predictive Algorithms in Data Mining for Cardiovascular Disease using WEKA Tool

Cardiovascular Disease (CVD) is the foremost cause of death worldwide that generates a high percentage Electronic Health Records (EHRs). Analyzing these complex patterns from EHRs tedious process. To address this problem, Medical Institutions requires effective Predictive Algorithms for Prognosis and Diagnosis Patients. Under work, current state-of-the-art studied to identify leading Algorithms. Further, algorithms namely Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), AdaBoost k-Nearest Neighbors (k-NN) analyzed against two datasets on open-source WEKA software. This work used similar structured i.e., Statlog Dataset Cleveland Dataset. For Pre-Processing Datasets, The missing values were replaced with Mean value later 10 Fold Cross-Validation was utilized evaluation. result performance analysis showed SVM outperforms other both datasets. an accuracy 84.156% dataset 84.074% dataset. LR ROC Area 0.9 findings will help understand importance usage automatic prediction CVD based symptoms.