A novel approach for predicting the length of hospital stay with DBSCAN and supervised classification algorithms
Publication Type:Journal Article
Source:IEEE conference proceedings, 2014.This is also extended to International Journal on Intelligent systems (2014)
Keywords:accuracy, Data mining, DBSCAN clustering algorithm, hospital performance monitoring, hospital resource consumption, Hospitals, learning (artificial intelligence), medical administrative data processing, Neural networks, patient stay length prediction, Pattern classification, pattern clustering, planning, Predictive models, supervised classification algorithm, Support vector machines, Training
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.
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