Prediction models for pre-operative patients : the case study of Chiang Rai Prachanukroh hospital

Name: Piyatida Watcharapasorn

Organization : Mae Fah Luang University

keyword: Pre-operative patients

LCSH: Information technology

Classification :.LCCS: Q325.5

LCSH: Machine learning -- Thailand -- Chiang Rai

LCSH: Computer algorithms

Abstract: The increasing number of surgical patients has resulted in increased attention being paid to the outcomes of surgical procedures. However, prior to undergoing any surgical procedure, pre-operative patients must be appropriately prepared by medical staff. Attention must be paid to gender, age, weight, stage of illness and overall patient health. In addition, physicians evaluate patient nutrition prior to operation. These factors all greatly impact the patient and the surgery, and it is extremely difficult to predict the surgical outcomes and post-surgical conditions. In this study, a classification model was designed to predict surgical outcomes. The model can be used by physicians to assess possible risks to patients after completion of the surgery, including infections and death. Data was collected from the Chiang Rai Nutrition Assessment (CNA), which was used to evaluate surgical patients at Chiang Rai Prachanukroh Hospital (Chiang Rai Regional Hospital) in 2014. First, data was prepared by inspecting the data. Erroneous data was corrected and missing values were compensated using sampled data. In addition, the SMOTE technique was applied to reduce imbalanced data from CNA records and the majority vote technique was implemented to increase forecasting accuracy. KNN, J48 and ADTree were used to predict mortality rate and KNN, Random Tree and Random forest were used to predict infection rates. The resulting predictive values can be used as a medical tool to assist physicians in the preparation of pre-operative and surgical patients to improve the post-operative patient conditions.

Mae Fah Luang University

Address: CHIANG RAI

Email: library@mfu.ac.th

Name: Nilubon Kurubanjerdjit

Role: Advisor

Created: 2017

Issued: 2561

Modified: 2018-10-05

Issued: 2018-10-05

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: Thesis Q325.5 P694p 2017

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Information Technology

Grantor: Mae Fah Luang University

©copyrights Mae Fah Luang University

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