Application of Machine Learning to Determine the Factors Affecting Deterioration in Patients with Chronic Kidney Disease

- Herwanto, Ali Khumaidi

Abstract


Hospital databases generally contain large amounts of data and various, but it has not been used optimally. It needs a technique that can utilize mountains of data into strategically valuable information. This paper will investigate ways to use hospital data to help determine the factors that influence the deterioration in patients with chronic kidney disease. The criteria for the selected patients were patients with a diagnosis of chronic kidney disease and chemotherapy treatment at least once. Three hundred seventy-six patients met these criteria. Subsequently, observation the patient's treatment course for three years. Ninety patients died in the hospital during that period. All the results of patients' blood tests were collected for further analysis. In forming the classification model, there are three stages carried out. The first stage deals with diverse, incomplete, and inconsistent data. Then through the process of changing continuous data into categorical data, each variable is classified into several categories. The next stage is to create a predictive model to determine the factors that influence the deterioration in patients with kidney failure using the Random Forest, Logistic Regression, and Decision Tree algorithms. Information of the classification model, 12 variables were selected, namely age, sex, and the results of clinical pathology laboratory examinations-Ureum, Thrombocyte, Natrium, Creatinine, Chloride, Kalium, Hemoglobin, Hematocrit, and Leukocytes. The three algorithms can classify training data with an accuracy of 98% (Random Forest), 83% (Logistic Regression), 98% (ID3).

Keywords


Chronic kidney disease; machine learning; classification; discretization; decision tree.

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References


C. Charles and A. H. Ferris, “Chronic Kidney Disease,†Prim. Care Clin. Off. Pract., vol. 47, no. 4, pp. 585–595, Dec. 2020, doi: 10.1016/j.pop.2020.08.001.

J. T. Daugirdas et al., “KDOQI Clinical Practice Guideline for Hemodialysis Adequacy: 2015 Update,†Am. J. Kidney Dis., vol. 66, no. 5, pp. 884–930, Nov. 2015, doi: 10.1053/j.ajkd.2015.07.015.

Z. H. Ong et al., “Sources of Distress Experienced by Parents of Children with Chronic Kidney Disease on Dialysis: A Qualitative Systematic Review,†J. Pediatr. Nurs., vol. 57, pp. 11–17, Mar. 2021, doi: 10.1016/j.pedn.2020.10.018.

C. I. Ossai and N. Wickramasinghe, “Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit – A critical overview,†Int. J. Med. Inform., vol. 150, p. 104469, Jun. 2021, doi: 10.1016/j.ijmedinf.2021.104469.

I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,†Artif. Intell. Med., vol. 23, no. 1, pp. 89–109, Aug. 2001, doi: 10.1016/S0933-3657(01)00077-X.

L. D’hooge, T. Wauters, B. Volckaert, and F. De Turck, “Inter-dataset generalization strength of supervised machine learning methods for intrusion detection,†J. Inf. Secur. Appl., vol. 54, p. 102564, Oct. 2020, doi: 10.1016/j.jisa.2020.102564.

C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu, “Integrating Machine Learning with Human Knowledge,†iScience, vol. 23, no. 11, p. 101656, Nov. 2020, doi: 10.1016/j.isci.2020.101656.

S. Senanayake, N. White, N. Graves, H. Healy, K. Baboolal, and S. Kularatna, “Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models,†Int. J. Med. Inform., vol. 130, p. 103957, Oct. 2019, doi: 10.1016/j.ijmedinf.2019.103957.

C. Sabanayagam et al., “A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations,†Lancet Digit. Heal., vol. 2, no. 6, pp. e295–e302, Jun. 2020, doi: 10.1016/S2589-7500(20)30063-7.

I. Alnazer et al., “Recent advances in medical image processing for the evaluation of chronic kidney disease,†Med. Image Anal., vol. 69, p. 101960, Apr. 2021, doi: 10.1016/j.media.2021.101960.

G. Lei, G. Wang, C. Zhang, Y. Chen, and X. Yang, “Using Machine Learning to Predict Acute Kidney Injury After Aortic Arch Surgery,†J. Cardiothorac. Vasc. Anesth., vol. 34, no. 12, pp. 3321–3328, Dec. 2020, doi: 10.1053/j.jvca.2020.06.007.

S. Nusinovici et al., “Logistic regression was as good as machine learning for predicting major chronic diseases,†J. Clin. Epidemiol., vol. 122, pp. 56–69, Jun. 2020, doi: 10.1016/j.jclinepi.2020.03.002.

D. A. Martinez et al., “Early Prediction of Acute Kidney Injury in the Emergency Department with Machine-Learning Methods Applied to Electronic Health Record Data,†Ann. Emerg. Med., vol. 76, no. 4, pp. 501–514, Oct. 2020, doi: 10.1016/j.annemergmed.2020.05.026.

N. N. Mahdi, “Constructing a Model with Binary Response to Some of the Factors Affecting the Incidence of Chronic Kidney Failure,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 2, p. 618, Apr. 2021, doi: 10.18517/ijaseit.11.2.14092.

I. A. U. Alnaqash and S. J. Abdel Sahib, “M.L. Estimator for Fuzzy Survival Function to the Kidney Failure Patients,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 2, p. 516, Apr. 2021, doi: 10.18517/ijaseit.11.2.14081.

W. H. Organization, “ICD-10 : international statistical classification of diseases and related health problems : tenth revision, 2nd ed,†2004.

K. K. R. Indonesia, “Pedoman Interpretasi Data Klinik,†2020.

I. Arjani, “Overview of Serum Ureum and Creatinine Levels in Chronic Kidney Desease Patients Undergoing Hemodialysis Therapy at Sanjiwani Hospital, Gianyar,†Meditory J. Med. Lab., vol. 4, no. 2, Jan. 2017, doi: 10.33992/m.v4i2.64.

J. S. Lees et al., “Kidney function and cancer risk: An analysis using creatinine and cystatin C in a cohort study,†EClinicalMedicine, vol. 38, p. 101030, Aug. 2021, doi: 10.1016/j.eclinm.2021.101030.

B. M. Kraemer, “Rethinking discretization to advance limnology amid the ongoing information explosion,†Water Res., vol. 178, p. 115801, Jul. 2020, doi: 10.1016/j.watres.2020.115801..

S. Misra and S. S. Ray, “Finding optimum width of discretization for gene expressions using functional annotations,†Comput. Biol. Med., vol. 90, pp. 59–67, Nov. 2017, doi: 10.1016/j.compbiomed.2017.09.010.

J. L. Flores, B. Calvo, and A. Perez, “Supervised non-parametric discretization based on Kernel density estimation,†Pattern Recognit. Lett., vol. 128, pp. 496–504, Dec. 2019, doi: 10.1016/j.patrec.2019.10.016..

I. Hasanah, E. Purwanti, and P. Widiyanti, “Design and Implementation of an Early Screening Application for Dengue Fever Patients Using Android-Based Decision Tree C4.5 Method,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 6, p. 2237, Dec. 2020, doi: 10.18517/ijaseit.10.6.5771.

S. Lebrun, Y. Xie, S. Chavez, R. Chan, and J. V. Jester, “An in vitro depth of injury prediction model for a histopathologic classification of EPA and GHS eye irritants,†Toxicol. Vitr., vol. 61, p. 104628, Dec. 2019, doi: 10.1016/j.tiv.2019.104628.

S. Saha, M. Saha, K. Mukherjee, A. Arabameri, P. T. T. Ngo, and G. C. Paul, “Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India,†Sci. Total Environ., vol. 730, p. 139197, Aug. 2020, doi: 10.1016/j.scitotenv.2020.139197.

H. Sujaini, “Image Classification of Tourist Attractions with K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 6, p. 2207, Dec. 2020, doi: 10.18517/ijaseit.10.6.9098.

S. Yang, J.-Z. Guo, and J.-W. Jin, “An improved Id3 algorithm for medical data classification,†Comput. Electr. Eng., vol. 65, pp. 474–487, Jan. 2018, doi: 10.1016/j.compeleceng.2017.08.005.

J. Vasquez and B. E. Comendador, “Competency Discovery System: Integrating the Enhanced ID3 Decision Tree Algorithm to Predict the Assessment Competency of Senior High School Students,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 1, p. 60, Jan. 2019, doi: 10.18517/ijaseit.9.1.7763.

A. Ramadhan, B. Susetyo, and - Indahwati, “Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 2, p. 501, Apr. 2021, doi: 10.18517/ijaseit.11.2.8878.

G. S. Saragih, Z. Rustam, D. Aldila, R. Hidayat, R. E. Yunus, and J. Pandelaki, “Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 5, p. 2177, Oct. 2020, doi: 10.18517/ijaseit.10.5.13000.

S. Asadi, S. Roshan, and M. W. Kattan, “Random forest swarm optimization-based for heart diseases diagnosis,†J. Biomed. Inform., vol. 115, p. 103690, Mar. 2021, doi: 10.1016/j.jbi.2021.103690.

S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,†Behav. Processes, vol. 148, pp. 56–62, Mar. 2018, doi: 10.1016/j.beproc.2018.01.004.




DOI: http://dx.doi.org/10.18517/ijaseit.12.4.16005

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