Amir Jamshidnezhad1, Ahmad Azizi1, Sara Rekabeslami Zadeh1, Saeed Shirali2, Maryam Hadadzadeh Shoushtari3, Yalda Sabaghan4, Vahideh Ziagham4, Mahsa Attarzadeh4. 1. Ahvaz Jundishapur University of Medical Sciences Department of Health Information Technology, Faculty of Allied Health Sciences Ahvaz Iran. 2. Ahvaz Jundishapur University of Medical Sciences Department of Laboratory Sciences, Faculty of Allied Health Sciences Ahvaz Iran. 3. Ahvaz Jundishapur University of Medical Sciences Faculty of Medicine Ahvaz Iran. 4. Ahvaz Imam Educational Hospital Ahvaz Iran.
Abstract
INTRODUCTION: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions. OBJECTIVES: To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain. METHODS: Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery as well as other detection methods in the previous studies. RESULTS: The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute appendicitis diagnosis. Experimental outcomes illustrated that the proposed algorithm improves considerably the optimization performance in the diagnostic problem with the accuracy rate of 89.9%. The mentioned rate was achieved while a limited range of factors as the input parameters were used in the hybrid model. CONCLUSION: The proposed differential diagnostic model can be used as a CDSS especially conditions in which access to costly equipment such as CT scans and Sonography tools are limited. The developed model improves the diagnosis time as well as the treatment costs for the patients with acute abdomen suspicious of acute appendicitis.
INTRODUCTION: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions. OBJECTIVES: To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain. METHODS: Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery as well as other detection methods in the previous studies. RESULTS: The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute appendicitis diagnosis. Experimental outcomes illustrated that the proposed algorithm improves considerably the optimization performance in the diagnostic problem with the accuracy rate of 89.9%. The mentioned rate was achieved while a limited range of factors as the input parameters were used in the hybrid model. CONCLUSION: The proposed differential diagnostic model can be used as a CDSS especially conditions in which access to costly equipment such as CT scans and Sonography tools are limited. The developed model improves the diagnosis time as well as the treatment costs for the patients with acute abdomen suspicious of acute appendicitis.
Authors: Steven S Raman; David S K Lu; Barbara M Kadell; Darko J Vodopich; James Sayre; Henry Cryer Journal: AJR Am J Roentgenol Date: 2002-06 Impact factor: 3.959
Authors: David G Bundy; Julie S Byerley; E Allen Liles; Eliana M Perrin; Jessica Katznelson; Henry E Rice Journal: JAMA Date: 2007-07-25 Impact factor: 56.272