Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Abstract

Due to the accessibility of data with multiple features, many feature determination techniques available in written form. These features promote data with extremely high measurement values. The feature determination strategy provides us with a way to reduce calculation time, improve prediction execution, and have a better understanding of data in machine learning, as well as a way to recognize applications. As pointed out by related works that have been reviewed, in general, existing works only focus on amplifying classification accuracy. For real-world applications, the selected subset of features must be continuous instead. In this research, proposes a sequential feature selection algorithm for detecting death events in heart disease patients during treatment to select the most important features. Several machine learning algorithms (LDA, RF, GBC, DT, SVM, and KNN) are used. In addition, the accuracy obtained by this method (SFS) is compared with the accuracy of the classifier. The confusion matrix, ROC curve, precision, recall rate, and f1-score are also calculated to verify the results obtained by the SFS algorithm. The experimental results show that for Random Forest Classifier_FS, the SFS method reaches 86.67% accuracy.