Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Abstract

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.

Post-Analysis of Predictive Modeling with an Epidemiological Example

“…Despite these limitations, the approach proposed is useful for many epidemiological studies, as the model accuracy has been reported to be far from 100% [ 33 , 34 , 35 , 36 ]. “Difficult” cases should be identified so that clinicians can revise the model predictions and use their expertise when the model is likely to make a mistake.…”

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT

“…MLCs for normal and all glaucomatous eyes are also constructed. These machine learning algorithms have been widely applied in various healthcare and/or medical informatics applications and do not have a prior assumption about data distribution [ 35 , 36 , 37 , 38 ]. The multivariate logistic regression (LGR) was used as a benchmark for comparison.…”

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

“…For example, to determine the relative risk factors about the one-year medical expense after discharge, each important variable could provide helpful information through different feature selection methods. Huang et al [5] point out that using fewer features was more efficient in model building.…”

“…This research used 44 variables [4,5,7,[26][27][28][29][30][31], which depended on the physician's clinical experience and literature review. Moreover, it used five different machine learning methods to predict after filtering factors, the highest score (10 points) was the most crucial factor, which will be the first on the rank; on the other hand, the lowest predictor was ranked the last (1 point).…”

“…However, the expenditure after CABG surgery remains poorly predicted. The various studies point out preoperative comorbidities, multiple complications, and medical expenses are essential variables that can affect the survival of CABG surgery patients [5][6][7].…”