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Abstract
Optimizing Buying Drugs Using Data mining
By
Mohammad Mahdi Toranji
Developing information technology’s application in health care systems leads to advantages including accessibility of data. Applying data mining methods on available data could improves management and decision making process. This study was aimed to evaluate various algorithms that have been used in data mining to define a model for prediction of medications utilization in hospitals. For this purpose we extracted data from health information system of Bam’s Pasteur hospital that was saved for 5 years. Models such as LSSVR, LR, BAGTREE, ADABOOST, SVR and MLP were evaluated in prediction of drug usage. Power of mentioned models for prediction was assessed according to MAE, RMSE, MSE and R2 measures. In conclusion BAGTREE model was revealed as best model.
Keywords : Hospital Information Systems, Buying Drugs, Prediction, Pharmacy
IN THE NAME OF GOD
Optimizing Buying Drugs Using Data mining
BY
Mohammad Mahdi Toranji
THESIS
SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER
OF SCIENCE (MSc.)
IN
Information Technology Engineering (e-Commerce)
SHIRAZ UNIVERSITY
SHIRAZ
ISLAMIC REPUBLIC OF IRAN
EVALUATED AND APPROVED BY THE THESIS COMMITTEE AS:
………………………… , Ph.D., PROF. Of (CHAIRMAN)
…….…………………… , PhD., PROF . Of
………………………… , Ph.D., ASSOCIATEPROF Of
Information Technology Engineering
January 2015
Shiraz University
Faculty of eLearning
M.S. Thesis
In Information Technology Engineering (ECommerce)
Optimizing Buying Drugs Using Data mining
By
Mohammad Mahdi Toranji
Supervised by
Dr. Reza Boostani
January 2015
1.Association
2.Prediction
3.Classification
Clustering 4.
5 hospital information system
6knowledge discovery in database (KDD)
7 Electronic Patient Record
8 Laboratory information system
9. Artificial Neural Network
10.Support Vector Machine
11.Support Vector Regression
12 Multi Layer Neural Network
13 Support Vector Regression
14 Bagging trees
15 Linear Regression
16 Least Square Support Vector Regression
17 Mean Absolute Error
18 Mean Square Error
19 Root Mean Square Error
20 Mean Absolute Percentage Error
21 Coefficient of Determination
22 Train
23 Test
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