Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

作     者：Salah Alzghoul Othman Smadi Ali Al Bataineh Mamon Hatmal Ahmad Alamm 

作者机构：Biomedical Engineering DepartmentThe Hashemite UniversityZarqaJordan Department of Electrical and Computer EngineeringNorwich UniversityNorthfieldVermont05663USA Department of Biochemistry and Molecular BiologyThe Hashemite UniversityZarqaJordan National Centre for Big Data Science and Artificial IntelligenceAmmanJordan 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第7期

页      面：455-470页

核心收录：

学科分类：1002[医学-临床医学] 0835[工学-软件工程] 100214[医学-肿瘤学] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：Aortic stenosis aortic valve replacement machine learning survival period enhancement artificial intelligence in cardiology 

摘      要：Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric records.A delay in surgical aortic valve replacement(SAVR)can potentially affect patients’quality of *** using ML algorithms,this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following *** study represents a novel approach that has the potential to improve decision-making and,ultimately,improve patient *** analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for *** divide the data into two groups:those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last *** then use six different ML algorithms,Support Vector Machine(SVM),Classification and Regression Tree(C and R tree),Generalized Linear(GL),Chi-Square Automatic Interaction Detector(CHAID),Artificial Neural Net-work(ANN),and Linear Regression(LR),to generate predictions for the best timing for *** results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival *** optimizing the timing of SAVR surgery using the SVM algorithm,we observed a significant improvement in the survival period after *** study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS.