## 1 Introduction  
Stroke is a leading cause of mortality and adult disability worldwide [1; 2]. It causes a huge financial burden on the healthcare system [3]. Stroke survivors are prone to recurrence of stroke. Approximately 7% to 12% of patients with a first ischemic stroke have stroke recurrence within one year [4; 5]. In addition, patients with stroke are likely to develop complications such as pneumonia, urinary tract infection, falls, etc., which may have a deleterious effect on the outcome of stroke [6]. As a result, a substantial proportion of stroke survivors are readmitted for various reasons after the initial hospitalization. In Taiwan, the 30-day readmission rate in patients with stroke was around 10% [7; 8], and the 1-year readmission rate was between 30% and 43% [7; 8; 9; 10]. Moreover, nearly 30% of the first-year medical cost for patients with stroke was spent on readmission [9]. Therefore, readmission after stroke is costly and needs more attention.  
Patients with stroke who are readmitted have greater levels of disability, a higher risk of mortality, and more medical resource utilization than those not readmitted [11; 12]. At the same time, risk-standardized mortality and readmission rates are used as indicators for hospital performance [13]. Even though it is arguable to link these indicators to reimbursement [14], the US Centers for Medicare & Medicaid Services (CMS) has implemented a program to reduce payments to hospitals with excess readmissions [15]. Specifically, for stroke care, CMS uses a hospital-level 30-day risk-standardized all-cause readmission measure to determine payments to hospitals [16]. Hospitals would thereby be penalized if their risk-standardized readmission rates were higher than expected [17]. Similarly, in Taiwan, the rate of unplanned 14-day readmission for the same or a related diagnosis and crude mortality rate are among the continuous monitoring indicators of care quality of Taiwan's hospital accreditation system [18]. While the hospital level is determined based on the results of hospital accreditation, it in turn determines the reimbursement a hospital will receive from the National Health Insurance program in Taiwan.  
Thorough knowledge of the risk and causes of readmission or mortality is thus essential for both quality improvement planning and healthcare resource allocation. Because the occurrence of medical complications increases the risk of readmission or mortality, aggressive management, and treatment of complications that are potentially modifiable might be able to reduce readmissions or mortality following stroke [6; 8; 19]. Furthermore, early risk assessment using information available upon admission might help identify high-risk patients for targeted interventions, which could possibly prevent avoidable readmissions, reduce mortality and improve functional outcomes, and even enhance the financial health of hospitals.  
Because machine learning (ML) techniques have been widely used in clinical decision support, this study aimed to use ML-based techniques to explore the predictive factors and to develop prediction models for readmission or mortality in patients hospitalized for stroke or transient ischemic attack (TIA). Specifically, this study compared prediction models that were developed using various ML techniques based on initial admission data from a large teaching hospital in Taiwan.