Volume 11, Issue 3 (9-2022)
JCHR 2022, 11(3): 158-164 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Karami A, Askarishahi M, Namiranian N. Comparison of Different Decision Tree Algorithms for Classification of Retinopathy Patients in
Yazd City, Central Part of Iran. JCHR 2022; 11 (3) :158-164
URL: http://jhr.ssu.ac.ir/article-1-908-en.html
URL: http://jhr.ssu.ac.ir/article-1-908-en.html
1- Shahid Sadoughi University of Medical Sciences and Health Services , amn.krm.rbt@gmail.com
2- Shahid Sadoughi University of Medical Sciences and Health Services
2- Shahid Sadoughi University of Medical Sciences and Health Services
Abstract: (825 Views)
Introduction: Diabetes is one of the most common diseases caused by metabolic disorders. It is the result of impaired secretion or function of insulin. The prevalence of diabetes is increasing rapidly. The aim of this study is to investigate the performance of different decision tree algorithms in the diagnosis of diabetic retinopathy. It was done using a database regarding diabetic patients. They were referred to Yazd Diabetes Research Center.
Method: This study was analytical cross-sectional. 2613 patients visited Yazd City's research and treatment center. Their demographic information was received in the first stage. Then, they were tested by the nursing team, and the patient's information form was completed by the respective nurse. After that, the descriptive indicators of mean, mode, median, variance, frequency, and percentage of missing data were observed. Four diagnostic models (Chadi), classification tree and regression (C and R), (Quest) and C 5.0 were compared. Authors evaluated the performance of these four models using three statistical criteria: accuracy, sensitivity, and specificity. Gains chart was used for more accurate comparison of models. SPSS MODELER V 18.0 software was used for data processing and modeling. The significance level was considered 5%.
Result: In this study, among the demographic and clinical variables, BMI, duration of disease, type of drug used, age, hypertension, gender, cholesterol, and hemoglobin A1c were entered in the final model. The dependent variable of retinopathy was investigated. It was based on the obtained criteria of accuracy (71.75), sensitivity (75.60), specificity (57.14) in the CART model; accuracy (65.84), sensitivity (65.86), specificity (65.76) of the Quest model; accuracy (69.33), sensitivity (67.35), specificity (76.81) of Chaid model; and accuracy (73.27), sensitivity (79.65), specificity (49.05) of Chaid model.
Conclusion: Based on the criteria of accuracy, sensitivity, specificity, and comparison of Gain Chart for four algorithms, Chaid algorithm showed better performance. Therefore, for further research, the authors suggest this algorithm
Method: This study was analytical cross-sectional. 2613 patients visited Yazd City's research and treatment center. Their demographic information was received in the first stage. Then, they were tested by the nursing team, and the patient's information form was completed by the respective nurse. After that, the descriptive indicators of mean, mode, median, variance, frequency, and percentage of missing data were observed. Four diagnostic models (Chadi), classification tree and regression (C and R), (Quest) and C 5.0 were compared. Authors evaluated the performance of these four models using three statistical criteria: accuracy, sensitivity, and specificity. Gains chart was used for more accurate comparison of models. SPSS MODELER V 18.0 software was used for data processing and modeling. The significance level was considered 5%.
Result: In this study, among the demographic and clinical variables, BMI, duration of disease, type of drug used, age, hypertension, gender, cholesterol, and hemoglobin A1c were entered in the final model. The dependent variable of retinopathy was investigated. It was based on the obtained criteria of accuracy (71.75), sensitivity (75.60), specificity (57.14) in the CART model; accuracy (65.84), sensitivity (65.86), specificity (65.76) of the Quest model; accuracy (69.33), sensitivity (67.35), specificity (76.81) of Chaid model; and accuracy (73.27), sensitivity (79.65), specificity (49.05) of Chaid model.
Conclusion: Based on the criteria of accuracy, sensitivity, specificity, and comparison of Gain Chart for four algorithms, Chaid algorithm showed better performance. Therefore, for further research, the authors suggest this algorithm
Review: Research |
Subject:
General
Received: 2022/06/21 | Accepted: 2022/09/19 | Published: 2022/10/19
Received: 2022/06/21 | Accepted: 2022/09/19 | Published: 2022/10/19
References
1. Elsalamony HA, Elsayad AM. Bank direct marketing based on neural network and C5. 0 Models. Int J Eng Adv Technol IJEAT. 2013;2(6).
2. Nisbet R, Elder J, Miner G. Handbook of statistical analysis and data mining applications: Academic Press; 2009.
3. Floares A, Birlutiu A, editors. Decision tree models for developing molecular classifiers for cancer diagnosis. The 2012 International Joint Conference on Neural Networks (IJCNN); 2012: IEEE. [DOI:10.1109/IJCNN.2012.6252781]
4. Kass GV. An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society: Series C (Applied Statistics). 1980;29(2):119-27. [DOI:10.2307/2986296]
5. Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural networks. 1990;3(5):551-60. [DOI:10.1016/0893-6080(90)90005-6]
6. Breiman L. Fried man JH, Olshen RA, Stone CJ. Classification and regression trees Belmont, California: Wadsworth International Group. 1984.
7. Lin C-L, Fan C-L. Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan. Journal of Asian Architecture and Building Engineering. 2019;18(6):539-53. [DOI:10.1080/13467581.2019.1696203]
8. Elsayad AM, Elsalamony H. Diagnosis of breast cancer using decision tree models and SVM. International Journal of Computer Applications. 2013;83(5). [DOI:10.5120/14445-2604]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
