Evaluation Of Classification Methods For The Prediction Of...

Evaluation of Classification Methods for the Prediction of Hospital Length of Stay using Medicare Claims data. Length of stay in hospital determines the quality of care and safety of patients. It depends on various factors and varies among medical cases with different conditions and complications. Length of stay depends on factors such as age, sex, co-morbidities, time between surgery and mobilization, severity of illness, etc [1]. It can be assumed that reduced length of stay hospital is associated with better health results and good quality of care. Length of stay in hospital also depends upon the quick response to the emergency medical case. The earlier the response the less is the length of stay and less likelihood of death†¦show more content†¦Unsupervised learning in data mining helps in clustering the data by determining their similarity, helping patterns to emerge. The supervised learning is used to classify new unknown data [4-7]. For the above scenarios, SPSS software [8] was for statistical analysis and WEKA 3.7 for classification software [9]. The data that was used for analysis is a 2012 Medicare Provider Analysis and Review (MEDPAR) file. The first scenario assumes that the patient has just been admitted and the information known about the patient is as follows: 1Patient demographics 2Information about their hospital 3Admission information The following assumptions are made for the second scenario: 1The patient has already been hospitalized for a few days. 2Diagnosis is already known. 3Procedures and other related hospital information. Let’s consider the first LOS cut-off point is 4 days and the second LOS cut off point is 12 days. Here we predict the LOS, if it is equal to 4days then it is approximately about 50% percentile of the LOS distribution and LOS equal to 12 days is approximately 90% percentile of the LOS distribution. In this experiment the performance of three classifiers are compared. The three classifiers used are Naà ¯ve Bayes, AdaBoost and C4.5 decision tree. The first classifier used is Naà ¯ve Bayes it is a probabilistic classifier based on Bayes theorem. The second classifier used is the Adaboost