Abstract
Prediction techniques represent an effective tool for knowledge discovery in huge and complex dataset in many fields including healthcare. The problem of healthcare is managing available medical resources and preparing plans for the future needs aiming to enhance medical services. This research provides an integrated prediction model to solve the problem above by analyzing medical data records and predicating the duration of future patient‘s hospitalization. The proposed model consists of three major stages; starting with preprocessing the data; applying prediction algorithm; and ending with evaluating the model based on real data. Our model used Gradient Boosting Machine (GBM) algorithm which reduce training error by building a sequence of decision trees. GBM is characterized by updating values of target feature after the construction of each decision tree. In this research, we tried to discover the effect of reducing the update process on terminal nodes that have lowest percentage of error, the results showed the ineffectiveness of reduction compared to the original. The research tried to determine the best measure for choosing splitter feature during the building of the decision tree, and the results showed that standard deviation is better than mean. The research also studied effect of changing values of GBM algorithm parameters in behavior of training process.
تاريخ النشر
01/01/2015
الناشر
مجلة جامعة بابل للعلوم الصرفة
الكلمات المفتاحية
Prediction Techniques, Healthcare, Data mining, Gradient Boosting Machine.
حيدر خضير عبد الله الفتلاوي | Hayder K. Fatlawi | 9705