Title :
A novel approach for predicting the length of hospital stay with DBSCAN and supervised classification algorithms
Author :
Panchami, V.U. ; Radhika, N.
Author_Institution :
Dept. of Comput. Sci. & Eng., Amrita Vishwa Vidyapeetham, Coimbatore, India
Abstract :
Patient length of stay is the most commonly employed outcome measure for hospital resource consumption and to monitor the performance of the hospital. Predicting the patient´s length of stay in a hospital is an important aspect for effective planning at various levels. It helps in efficient utilization of resources and facilities. So, there exist a strong demand to make accurate and robust models to predict length of stay. This paper analyzes various methods for length of stay prediction, its advantages and disadvantages and proposes a novel approach for predicting whether the length of stay of the patient is greater than one week. The approach uses DBSCAN clustering to create the training set for classification. The prediction models are compared using accuracy, precision and recall and found that using DBSCAN as a precursor to classification gives better results.
Keywords :
hospitals; learning (artificial intelligence); medical administrative data processing; pattern classification; pattern clustering; planning; DBSCAN clustering algorithm; hospital performance monitoring; hospital resource consumption; patient stay length prediction; planning; supervised classification algorithm; Accuracy; Data mining; Hospitals; Neural networks; Predictive models; Support vector machines; Training;
Conference_Titel :
Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the
Conference_Location :
Bangalore
Print_ISBN :
978-1-4799-2258-1
DOI :
10.1109/ICADIWT.2014.6814663