Predicting Hospital Patient Readmission by Analyzing Electronic Health Record with Interpretable Machine Learning
Published in Eurasian Journal of Mathematical and Computer Applications, 2024
This research investigates predicting whether a patient will be readmitted or not based on historical data. We employ eleven machine learning algorithms, namely Logistic Regression (LR), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), Naive Bayes (NB) [3], Decision Tree (DT), Support Vector Machines (SVM), Bagging Classifier (BC), Random Forest (RF), Extra Trees (ET), Adaboost (AB), and Stochastic Gradient Boosting (SGB). Additionally, we also investigate the most crucial features that have the most significant impact on predicting hospital readmissions.