Background: Early speculation of cardiovascular disease can help determine the lifestyle change options of high-risk patients, thereby reducing difficulties. We propose a coronary heart disease data set analysis technique to predict people’s risk of danger based on people’s clinically determined history. The methods introduced may be integrated into multiple uses, such for developing decision support system, developing a risk management network, and help for experts and clinical staff.
Methods: We employed the Framingham Heart study dataset, which is publicly available Kaggle, to train several machine learning classifiers such as logistic regression (LR), K-nearest neighbor (KNN), Naïve Bayes (NB), decision tree (DT), random forest (RF) and gradient boosting classifier (GBC) for disease prediction. The p-value method has been used for feature elimination, and the selected features have been incorporated for further prediction. Various thresholds are used with different classifiers to make predictions. In order to estimating the precision of the classifiers, ROC curve, confusion matrix and AUC value are considered for model verification. The performance of the six classifiers is used for comparison to predict chronic heart disease (CHD).
Results: After applying the p-value backward elimination statistical method on the 10-year CHD data set, 6 significant features were selected from 14 features with p <0.5. In the performance of machine learning classifiers, GBC has the highest accuracy score, which is 87.61%.
Conclusions: Statistical methods, such as the combination of p-value backward elimination method and machine learning classifiers, thereby improving the accuracy of the classifier and shortening the running time of the machine.
CALIBRATION OF VARIOUS OPTIMIZED MACHINE LEARNING CLASSIFIERS IN NETWORK INTRUSION DETECTION SYSTEM ON THE REALISTIC CYBER DATASET CSE-CIC-IDS2018 USING CLOUD COMPUTING
