Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning

Fatima Alshakhs; Hana Alharthi; Nida Aslam; Irfan Ullah Khan; Mohamed Elasheri

doi:10.2147/IJGM.S250334

Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning

Int J Gen Med. 2020 Oct 2:13:751-762. doi: 10.2147/IJGM.S250334. eCollection 2020.

Authors

Fatima Alshakhs¹, Hana Alharthi¹, Nida Aslam², Irfan Ullah Khan², Mohamed Elasheri³

Affiliations

¹ Department of Health Information Management & Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam 34221-4237, Saudi Arabia.
² Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 34221-4237, Saudi Arabia.
³ Department of Cardiac Surgery, Saud Albabtain Cardiac Centre, Dammam 32245, Saudi Arabia.

Abstract

Purpose: Predictive analytics (PA) is a new trending approach in the field of healthcare that uses machine learning to build a prediction model using supervised learning algorithms. Isolated coronary artery bypass grafting (iCABG), an open-heart surgery, is commonly performed in the treatment of coronary heart disease.

Aim: The aim of this study was to develop and evaluate a model to predict postoperative length of stay (PLoS) for iCABG patients using supervised machine learning techniques, and to identify the features with the highest contribution to the model.

Methods: This is a retrospective study that uses historic data of adult patients who underwent isolated CABG (iCABG). After initial data pre-processing, data imputation using the kNN method was applied. The study used five prediction models using Naïve Bayes, Decision Tree, Random Forest, Logistic Regression and k Nearest Neighbor algorithms. Data imbalance was managed using the following widely used methods: oversampling, undersampling, "Both", and random over-sampling examples (ROSE). The features selection process was conducted using the Boruta method. Two techniques were applied to examine the performance of the models, (70%, 30%) split and cross-validation, respectively. Models were evaluated by comparing their performance using AUC and other metrics.

Results: In the final dataset, six distinct features and 621 instances were used to develop the models. A total of 20 models were developed using R statistical software. The model generated using Random Forest with "Both" resampling method and cross-validation technique was deemed the best fit (AUC=0.81; F1 score=0.82; and recall=0.82). Attributes found to be highly predictive of PLoS were pulmonary artery systolic, age, height, EuroScore II, intra-aortic balloon pump used, and complications during operation.

Conclusion: This study demonstrates the significance and effectiveness of building a model that predicts PLoS for iCABG patients using patient specifications and pre-/intra-operative measures.

Keywords: CABG; LoS; classifiers; predictive analytics.