Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2020-06, Vol.138, p.104134-104134, Article 104134
• Main Authors: Hamedan, Farahnaz, Orooji, Azam, Sanadgol, Houshang, Sheikhtaheri, Abbas
• Format: Article
• Language: English
• Subjects: Chronic kidney disease; Clinical decision support system; Expert system; Fuzzy logic
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2020.104134

•Early diagnosis and prediction of chronic kidney disease (CKD) may prevent many negative outcomes of this disease.•Our fuzzy expert system predicts CKD with 92.13 %, 95.37 % and 88.88 % accuracy, sensitivity and specificity, respectively.•The performance of the system was acceptable against noisy data in all input variables. 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. 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. 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. Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.