# Decision Support Software for Forecasting Patient's Length of Stay
## 2 Related Work  
Hospitals are daily faced with a significant uncertainty which is mainly based on the LoS of hospitalized patients. As future admission requests appear to be a more complicated problem within an effective and long-term healthcare system planning, accurate prediction of in-hospital stay duration would allow, in short-term, for efficient human resources allocation and facilitiesoccupancy. LoS prediction is a substantial problem which attracted research community's attention since the 1960s [12; 13] by employing statistical methods. Since then, several scientific fields have risen, providing mathematical and computing classification and prediction techniques. Following the evolution of machine learning and data mining, research efforts focused on employing relevant algorithms in the field of LoS prediction. [PERSON] et al. [14] presented an excellent survey, describing in detail a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. Furthermore, they focused on the methods of analyzing length of stay and mortality prediction and provided a classification and evaluation of these methods with a grouping of relevant research papers published in the last 20 years. [PERSON] et al., [2] used several data mining techniques to extract useful knowledge and developed an accurate model to predict the LoS of cardiac patients. The dataset used in their study consisted of 4948 instances from patients with coronary artery disease. Their extended analysis revealed that a LoS greater than 10 days was associated with comorbidity and diastolic blood pressure features. Based on their numerical experiments, the authors concluded that their proposed ensemble algorithm exhibited the best performance than any individual algorithm, presenting 98.2% of successful classification. Moreover, they stated that there was a significant tendency for LoS to be longer in patients with lung or respiratory disorders and high blood pressure which implies that comorbidities such as lung disorders and hemorrhage have a significant impact on long LoS. [PERSON] et al. [13] compared and discussed the performance of various machine learning algorithms for the prediction of short-term vs. long-term LoS of hospitalized diabetic patients, where short-term is defined as less than 3 days. In their framework, they used 10,000 patients' records from the HCUP Nationwide Inpatient Sample database where each record contains several features including demographics, hospital information, admission type, number of diagnoses, health insurance status, total hospital charges and risk/severity measures. Their experimental analysis indicated that support vector machine constitutes the most promising method for predicting short-term LoS in hospitalized diabetic patients. In more recent works, [PERSON] et al. [15] performed a two stage LoS prediction: the predischarge and the preadmission. The predischarge stage uses all the available data of in-hospital patients, while the preadmission one uses only the data available before a patient's admission in the hospital. The prediction results for predischarge patients were used to evaluate the LoS prediction performance at the preadmission stage. They collected data from 2377 patients of cardiovascular disease with one of the three primary diagnoses: Coronary Atherosclerosis (CAS), Heart Failure (HF) and Acute Myocardial Infarction (AMI). Their proposed classification model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For HF/AMI patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of two days was allowed. [PERSON] et al. [16] developed an ensemble method to systematically rank, select, and combine machine learning algorithms to build a model for the prediction of patients' LoS following craniotomy for brain tumor.