Farahnaz Hamedan1, Azam Orooji2, Houshang Sanadgol3, Abbas Sheikhtaheri4. 1. School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran. 2. School of Medicine, North Khorasan University of Medical Sciences (NKUMS), North Khorasan, Islamic Republic of Iran. 3. School of Medicine, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran. 4. Health Management and Economics Research Center, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran. Electronic address: sheikhtaheri.a@iums.ac.ir.
Abstract
BACKGROUND AND OBJECTIVES: Diagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data. METHODS: At first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets. RESULTS: We selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively. CONCLUSION: Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.
BACKGROUND AND OBJECTIVES: Diagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data. METHODS: At first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets. RESULTS: We selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively. CONCLUSION: Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.
Authors: Alberto Comesaña-Campos; Manuel Casal-Guisande; Jorge Cerqueiro-Pequeño; José-Benito Bouza-Rodríguez Journal: Int J Environ Res Public Health Date: 2020-11-20 Impact factor: 3.390