Evaluation Of Classification Methods For The Prediction Of Hospital Length

1320 Words Nov 21st, 2014 6 Pages
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…
2>Diagnosis is already known.

3>Procedures 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 Naïve Bayes,

AdaBoost and C4.5 decision tree. The first classifier used is Naïve Bayes it is a probabilistic classifier

based on Bayes theorem. The second classifier used is the Adaboost classifier which uses the boosting

techniques, it combines various weak classifiers with the weighted majority voting scheme [10]. The third

classifier is the C4.5 decision classifier which builds a tree structure in top down, divide-and conquer

manner [11].

By using all these classifiers in the experiment the average LOS was calculated as 5.79days and median

was equal to 4 days.

Performance of all the classifiers for scenario 1:

a>Class-Length of stay>4 days

1> Naïve Bayes – It classified 66.4% of our instances correctly. It performed well on short stay. It

misclassified the

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