Design and implementation of an intelligent clinical decision support system for diagnosis and prediction of chronic kidney disease

authors:

avatar Ali Valinejadi , *

Koomesh: Vol.24, issue 4; 484-495
article type: issue_documents_importer
received: August 05, 2021
accepted: November 02, 2021

how to cite: Valinejadi A. Design and implementation of an intelligent clinical decision support system for diagnosis and prediction of chronic kidney disease. koomesh. 2024;24(4):e152757. 

Abstract

Introduction: Chronic kidney disease (CKD) is one of the most important public health concerns worldwide. The steady increase in the number of people with End-stage renal disease (ESRD) needing a kidney transplant to survive and incur high costs, highlights early diagnosis and treatment of the disease. This study aimed to design a Clinical Decision Support System (CDSS) for diagnosing CKD and predicting the advanced stage to achieve better management and treatment of the disease. Materials and Methods: In this retrospective and developmental study, we studied the records of 600 suspected CKD cases with 22 variables referred to ShahidLabbafinejad Hospital in Tehran from 2019 to 2020. Data mining algorithms such as Naïve Bayesian, Random Forest, Multilayer Perceptron neural network, and J-48 decision tree were developed based on extracted variables. Then the recital of selected models was compared by some performance indices and 10-fold cross-validation. Finally, the most appropriate prediction model in terms of performance was implemented using the C # programming language. Results: Random Forest classification algorithm with an accuracy of 99.8% and 88.66%, specificity of 100% and 93.8%, the sensitivity of 99.75% and 88.7%, f-measure of 99.8% and 88.7%, kappa score of 99.4% and 82.73%, and ROC of 100% and 90.52% was identified as the best data mining model for CKD diagnosis and prediction respectively. Conclusion: The developed MC-DMK system based random Forestcan be used practically in clinical settings.

References

  • 1.

    Assari S. Racial disparities in chronic kidney diseases in the United States; a pressing public health challenge with social, behavioral and medical causes. J Nephropharmacol 2016; 5: 4-6.https://doi.org/10.1007/s40615-016-0239-7PMid:27270925.

  • 2.

    Formica RN Jr., Gill JS. Should the United States employfree market practices to solve the hidden public health crisis of chronic kidney disease? Am J Transplant 2020; 20: 1217-1218.https://doi.org/10.1111/ajt.15718PMid:31746545.

  • 3.

    Goraya N, Simoni J, Jo CH, Wesson DE. Treatment of metabolic acidosis in patients with stage 3 chronic kidney disease with fruits and vegetables or oral bicarbonate reduces urine angiotensinogen and preserves glomerular filtration rate. Kidney Int 2014; 86: 1031-1038.https://doi.org/10.1038/ki.2014.83PMid:24694986.

  • 4.

    Tuganbekova S, Gaipov A, Turebekov Z, Saparbayev S, Shaimardanova G, Popova N, et al. Fetal renal stem cell transplant in nephrotic and nonnephrotic glomerulonephritis with stage 2-4 chronic kidney disease: potential effect on proteinuria and glomerular filtration rate. Exp Clin Transplant 2015; 13: 156-159.

  • 5.

    Lee YB, Han K, Kim B, Jun JE, Lee SE, Ahn J, et al. Risk of end-stage renal disease from chronic kidney disease defined by decreased glomerular filtration rate in type 1 diabetes: A comparison with type 2 diabetes and the effect of metabolic syndrome. Diabetes Metab Res Rev 2019; 35: e3197.https://doi.org/10.1002/dmrr.3197.

  • 6.

    Roshanaei G, Omidi T, Faradmal J, Safari M, Poorolajal J. Determining affected factors on survival of kidney transplant in living donor patients using a random survival forest. Koomesh 2018; 20: 517-523. (Persian).

  • 7.

    van Haalen H, Jackson J, Spinowitz B, Milligan G, Moon R. Impact of chronic kidney disease and anemia on health-related quality of life and work productivity: analysis of multinational real-world data. BMC Nephrol 2020; 21: 88.https://doi.org/10.1186/s12882-020-01746-4PMid:32143582 PMCid:PMC7060645.

  • 8.

    Tabrizi R, Zolala F, Nasirian M, Baneshi MR, Etminan A, Sekhavati E, et al. Estimation of the prevalence of chronic kidney disease: The results of a model based estimation in Kerman, Iran. Med J Islamic Repub Iran 2016; 30: 338. (Persian).

  • 9.

    Keshmiri YS, Mirzaie SK, Sali S, Yadegarynia D, Abolghasemi S, Tehrani S, et al. Evaluation of symptoms, radiological findings, laboratory data and outcome in COVID-19 patients with chronic kidney disease at Tehran, Iran. 2020. (Persian).https://doi.org/10.21203/rs.3.rs-101128/v1.

  • 10.

    Hosseinpanah F, Kasraei F, Nassiri AA, Azizi F. High prevalence of chronic kidney disease in Iran: a large population-based study. BMC Public Health 2009; 9: 44.https://doi.org/10.1186/1471-2458-9-44PMid:19183493 PMCid:PMC2658666.

  • 11.

    Sharifian Dorche M, Esfandiar N, Rahdar M. Correlation between anteroposterior renal pelvic diameter and vesicoureteral reflux in congenital hydronephros. Koomesh 2021; 23: 227-232. (Persian).https://doi.org/10.52547/koomesh.23.2.227.

  • 12.

    Kafle K, Balasubramanya S, Horbulyk T. Prevalence of chronic kidney disease in Sri Lanka: a profile of affected districts reliant on groundwater. Sci Total Environment 2019; 694: 133767.https://doi.org/10.1016/j.scitotenv.2019.133767PMid:31756806.

  • 13.

    Aghlmand S, Rahimi B, Farrokh-Eslamlou H, Nabilou B, Yusefzadeh H. Determinants of Iran's bilateral intra-industry trade in pharmaceutical industry. Iran J Pharmace Res 2018; 17: 822. (Persian).

  • 14.

    Shabaninejad H, Yusefzadeh H, Mehralian G, Rahimi B. The structure of the world pharmaceutical market: prioritizing Iran's target export markets. Iran J Pharmace Res 2019; 18: 546. (Persian).

  • 15.

    Yusefzadeh H, Hadian M, Gorji HA, Ghaderi H. Assessing the factors associated with Iran's intra-industry trade in pharmaceuticals. Global J Health Sci 2015; 7: 311. (Persian).https://doi.org/10.5539/gjhs.v7n5p311.

  • 16.

    Yusefzadeh H, Rezapour A, Lotfi F, Azar FE, Nabilo B, Gorji HA, et al. A Study of comparative advantage and intra-industry trade in the pharmaceutical industry of Iran. Global J Health Sci 2015; 7: 295. (Persian).https://doi.org/10.5539/gjhs.v7n6p295.

  • 17.

    Fazaeli AA, Fazaeli AA, Hamidi Y, Moeini B, Valinejadi A. Analysis of iranian household financial participation in the health system: decomposition of the concentration index approach. Koomesh 2018; 358-368. (Persian).

  • 18.

    Shojaei S, Yousefi M, Ebrahimipour H, Valinejadi A, Tabesh H, Fazaeli S. Catastrophic health expenditures and impoverishment in the households receiving expensive interventions before and after health sector evolution plan in Iran: Evidence from a big hospital. Koomesh 2018; 283-290. (Persian).

  • 19.

    Yousefi M, Aliani S, Valinejadi A, Rezazadeh A, Khorsand A, Fazaeli S, Ebrahimipour H. Effect of" Iran's health system evolution plan" and" tariff change" on financial performance of para-clinic units in a big tertiary hospital in Iran. Koomesh 2018; 20. (Persian).

  • 20.

    Askari-Majdabadi H, Valinejadi A, Mohammadpour A, Bouraghi H, Abbasy Z, Alaei S. Use of health information technology in patients care management: a mixed methods study in Iran. Acta Informatica Medica 2019; 27: 311.https://doi.org/10.5455/aim.2019.27.311-317PMid:32210498 PMCid:PMC7085310.

  • 21.

    Mohammadi A, Valinejadi A, Sakipour S, Hemmat M, Zarei J, Majdabadi HA. Improving the distribution of rural health houses using elicitation and GIS in Khuzestan province (the southwest of Iran). Int J Health Policy Manag 2018; 7: 336. (Persian).https://doi.org/10.15171/ijhpm.2017.101PMid:29626401 PMCid:PMC5949224.

  • 22.

    Milani RV, Oleck SA, Lavie CJ. Medication errors in patients with severe chronic kidney disease and acute coronary syndrome: the impact of computer-assisted decision support. Mayo Clin Proc 2011; 86: 1161-1164.https://doi.org/10.4065/mcp.2011.0290PMid:22134934 PMCid:PMC3228615.

  • 23.

    Samal L, D'Amore JD, Bates DW, Wright A. Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces. J Am Med Inform Assoc 2017; 24: 1111-1115.https://doi.org/10.1093/jamia/ocx065PMid:29016969 PMCid:PMC6580936.

  • 24.

    Alipour J, Safari Lafti S, Askari Majdabadi H, Yazdiyani A, Valinejadi A. Factors affecting hospital information system acceptance by caregivers of educational hospitals based on technology acceptance model (TAM): A study in Iran. Iioab J 2016; 119-123.

  • 25.

    Ziari A, Ansari M, Valinejadi A. The gap between the service quality and patients' expectations in amir-al-momenin hospital of Semnan university of medical sciences in 2016, Semnan, Iran. Koomesh 2018; 221-227. (Persian).

  • 26.

    Ennis J, Gillen D, Rubenstein A, Worcester E, Brecher ME, Asplin J, Coe F. Clinical decision support improves physician guideline adherence for laboratory monitoring of chronic kidney disease: a matched cohort study. BMC Nephrol 2015; 16: 163.https://doi.org/10.1186/s12882-015-0159-5PMid:26471846 PMCid:PMC4608162.

  • 27.

    Exarchos I, Rogers AA, Aiani LM, Gross RE, Clifford GD, Pedersen NP, Willie JT. Supervised and unsupervised machine learning for automated scoring of sleep-wake and cataplexy in a mousemodel of narcolepsy. Sleep 2020; 43.https://doi.org/10.1093/sleep/zsz272PMid:31693157 PMCid:PMC7215268.

  • 28.

    Singh Pathania Y, Budania A. Artificial intelligence in dermatology: "unsupervised" versus "supervised" machine learning. Int J Dermatol 2021; 60: e28-e29.https://doi.org/10.1111/ijd.15288PMid:33128460.

  • 29.

    Afhami N. Prediction of diabetic chronic kidney disease progression using data mining techniques. Int J Comput Sci Engin 2018; 7: 35-40.

  • 30.

    Boukenze B, Haqiq A, Mousannif H, editors. Predicting chronic kidney failure disease using data mining techniques. Int Symp Ubiquitous Networking 2016.https://doi.org/10.5121/csit.2016.60501.

  • 31.

    Wu B, Li D, Xu T, Luo M, He Z, Li Y. Proton pump inhibitors associated acute kidney injury and chronic kidney disease: data mining of US FDA adverse event reporting system. Sci Rep 2021; 11: 3690.https://doi.org/10.1038/s41598-021-83099-yPMid:33574396 PMCid:PMC7878877##.

  • 32.

    Xia P, Gao K, Xie J, Sun W, Shi M, Li W, et al. Data mining-based analysis of chinese medicinal herb formulae in chronic kidney disease treatment. Evid Based Complement Alternat Med 2020; 2020: 9719872.https://doi.org/10.1155/2020/9719872PMid:32047530 PMCid:PMC7003280.

  • 33.

    Kumar A, Kumar P, Srivastava A, Kumar VA, Vengatesan K, Singhal A, editors. Comparative analysis of data mining techniques to predict heart disease for diabetic patients. Int Confer Adv Comput Data Sci 2020.https://doi.org/10.1007/978-981-15-6634-9_46.

  • 34.

    Chaurasia V, Pal S, Tiwari BB. Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology. 2018 Jun;12 (2):119-26https://doi.org/10.1177/1748301818756225.

  • 35.

    Amin MS, Chiam YK, Varathan KD. Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics. 2019 Mar 1;36:82-93.https://doi.org/10.1016/j.tele.2018.11.007.

  • 36.

    Tougui I, Jilbab A, El Mhamdi J. Heart disease classification using data mining tools and machine learning techniques. Health and Technology. 2020 Sep;10(5):1137-44.https://doi.org/10.1007/s12553-020-00438-1.

  • 37.

    Patwardhan MB, Kawamoto K, Lobach D, Patel UD, Matchar DB. Recommendations for a clinical decision support for the management of individuals with chronic kidney disease. Clin J Am Soc Nephrol 2009; 4: 273-283.https://doi.org/10.2215/CJN.02590508PMid:19176797 PMCid:PMC2637586.

  • 38.

    Rashed-Al-Mahfuz M, Haque A, Azad A, Alyami SA, Quinn JM, Moni MA. Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in Low-Cost diagnostic screening. IEEE J Transl Eng Health Med 2021; 9: 1-11.https://doi.org/10.1109/JTEHM.2021.3073629https://doi.org/10.1109/JTEHM.2021.3050925PMid:33542859 PMCid:PMC7851059.

  • 39.

    Khoong EC, Karliner L, Lo L, Stebbins M, Robinson A, Pathak S, et al. A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience. JMIR Res Protoc 2019; 8: e14022.https://doi.org/10.2196/14022PMid:31199334 PMCid:PMC6594214.

  • 40.

    Pesce F, Diciolla M, Binetti G, Naso D, Ostuni VC, Di Noia T, et al. Clinical decision support system for end-stage kidney disease risk estimation in IgA nephropathy patients. Nephrol Dial Transplant 2016; 31: 80-86.https://doi.org/10.1093/ndt/gfv232PMid:26047632.

  • 41.

    Yadollahpour A, Nourozi J, Mirbagheri SA, Simancas-Acevedo E, Trejo-Macotela FR. Designing and implementing an ANFIS based medical decision support system to predict chronic kidney disease progression. Front Physiol 2018; 9: 1753.https://doi.org/10.3389/fphys.2018.01753PMid:30574095 PMCid:PMC6291481.

  • 42.

    Afrash MR, Khalili M, Salekde MS. A comparison of data mining methods for diagnosis and prognosis of heart disease. International Journal of Advanced Intelligence Paradigms. 2020;16 (1):88-97.https://doi.org/10.1504/IJAIP.2020.106692.

  • 43.

    Hossin M, SulaimanMN. A review on evaluation metrics for data classification evaluations. Int J Data Min Knowledge Manag Proc 2015; 5: 1.

  • 44.

    Fraser SD, Roderick PJ, May CR, McIntyre N, McIntyre C, Fluck RJ, Shardlow A, Taal MW. The burden of comorbidity in people with chronic kidney disease stage 3: a cohort study. BMC Nephrol 2015; 16: 1-11.https://doi.org/10.1186/s12882-015-0189-zPMid:26620131 PMCid:PMC4666158.

  • 45.

    Krishnamurthy S, Ks K, Dovgan E, Lutrek M, Gradiek Pileti B, Srinivasan K, et al, editors. Machine learning prediction models for chronic kidney disease using national health insurance claim data in Taiwan. Healthcare; 2021: Multidisciplinary Digital Publishing Institute.https://doi.org/10.1101/2020.06.25.20139147.

  • 46.

    RoyMS, Ghosh R, Goswami D, Karthik R, editors. Comparative analysis of machine learning methods to detect chronic kidney disease. J Phys Confer Series 2021; IOP Publishing.https://doi.org/10.1088/1742-6596/1911/1/012005.

  • 47.

    Darveshwala AY, Singh D, Farooqui Y, editors. Chronic kidney disease stage identification in HIV infected patients using machine learning. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC); 2021; IEEE.https://doi.org/10.1109/ICCMC51019.2021.9418430.

  • 48.

    Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J, et al. Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study. Comput Biol Med 2019; 109: 101-111.https://doi.org/10.1016/j.compbiomed.2019.04.017PMid:31054385.

  • 49.

    Sobrinho A, Queiroz ACDS, Da Silva LD, Costa ED, Pinheiro ME, Perkusich A. Computer-aided diagnosis of chronic kidney disease in developing countries: A comparative analysis of machine learning techniques. IEEE Access 2020; 8: 25407-25419.https://doi.org/10.1109/ACCESS.2020.2971208.

  • 50.

    Abdullah AA, Hafidz SA, Khairunizam W, editors. Performance comparison of machine learning algorithms for classification of chronic kidney disease (CKD). J Phys Confer Series 2020; IOP Publishing.https://doi.org/10.1088/1742-6596/1529/5/052077.

  • 51.

    Senan EM, Al-Adhaileh MH, Alsaade FW, Aldhyani TH, Alqarni AA, Alsharif N, et al. Diagnosis of chronic kidney disease using effective classification algorithms and recursive feature elimination techniques. J Health Care Engin 2021; 2021.https://doi.org/10.1155/2021/1004767PMid:34211680 PMCid:PMC8208843.

  • 52.

    Al-Hyari AY, Al-Taee AM, Al-Taee MA, editors. Clinical decision support system for diagnosis and management of chronic renal failure. 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT); 2013; IEEE.https://doi.org/10.1109/AEECT.2013.6716440.

  • 53.

    Levey AS, Coresh J. Chronic kidney disease. The lancet 2012; 379: 165-180.https://doi.org/10.1016/S0140-6736(11)60178-5.