Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Abstract

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

“…These ML methods can improve predictions by utilizing higher-dimensional, complex, and nonlinear relationships between variables [16]. These methods also have been used to predict hospital readmission in various studies [5,[16][17][18]. For instance, Lorenzoni et al [18] compared the performance of eight ML methods to predict hospitalization in HF patients.…”

“…These methods also have been used to predict hospital readmission in various studies [5,[16][17][18]. For instance, Lorenzoni et al [18] compared the performance of eight ML methods to predict hospitalization in HF patients. Their results showed that the generalized linear model net had the best performance.…”

“…If missingness is completely random (i.e., MCAR), it can be ignored [20]; otherwise, removing incomplete data may lead to bias and reduce the power of ML methods [20,21]. To overcome this problem, imputation methods such as median, mean, and multiple imputations can be used [5,18]. ML methods are nonparametric methods that require no distributional assumptions.…”

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

“…From the clinical point of view, MLTs represent a promising opportunity to develop models able to predict hospital admissions/readmissions of HF patients [ 7 ] and hard in-hospital outcomes such as cardiac arrest, death, and the development of new severe clinical conditions. Churpek et al conducted a multicenter study using machine learning methods for predicting clinical deterioration in the wards, including cardiac arrest, ICU transfer, and death.…”

“…Several methods have been employed to develop prediction models and risk scores in patients with cardiovascular diseases, from traditional statistical approaches to more advanced machine learning techniques (MLTs). In the adult population with HF, machine learning algorithms create risk scores estimating the likelihood of a heart failure diagnosis and the probability of outcomes such as all-cause mortality, cardiac death, and hospitalization [ 4 , 5 , 6 , 7 ]. MLTs are also increasingly used for hard outcome prediction in the clinical setting (e.g., in-hospital cardiac arrest) since they present several advantages over traditional methods and show promising performance and better power than existing prediction systems [ 8 , 9 ].…”