Skip to main content

Advertisement

SpringerLink
Log in

A novel enhanced decision tree model for detecting chronic kidney disease

  • Original Article
  • Published:
Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

Prediction of diseases is sensitive as any error can result in the wrong person's treatment or not treating the right patient. Besides, some features distinguish a disease from curable to fatal or curable to chronic disease. Data mining techniques have been widely used in health-related research. The researchers, so far, could attain around 97 percent accuracy using several methods. Some researchers have demonstrated that the selection of correct features increases the prediction accuracy. This research work propose a method to distinguish between chronic and non-chronic kidney disease, identify its crucial features without reducing the accuracy of prediction, and a prediction algorithm to eliminate the possibility of under or overfitting. This study uses the recursive feature elimination (RFE) method that selects an optimal subset of features and an ensemble algorithm, the enhanced decision tree (EDT), to predict the disease. The results obtained in this paper show that the accuracy level of EDT is not changed with the removal of less significant features, thus enabling the decision-makers to concentrate on few features to reduce time and error of treatment. EDT establishes substantially high consistency in predicting, with or without feature selection, the disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Alaiad A, Najadat H, Mohsen B, Balhaf K (2020) Classification and association rule mining technique for predicting chronic kidney disease. J Inf Knowl Manag 19(01):2040015

    Google Scholar 

  • Alasker H, Alharkan S, Alharkan W, Zaki A, Riza LS (2017) Detection of kidney disease using various intelligent classifiers. In: 2017 3rd international conference on science in information technology (ICSITech). IEEE, New York, pp 681–684

  • Al-Hadeethi H, Abdulla S, Diykh M, Deo RC, Green JH (2020) Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications. Expert Syst Appl 161:113676

    Google Scholar 

  • Aljaaf AJ, Al-Jumeily D, Haglan HM, Alloghani M, Baker T, Hussain AJ, Mustafina J (2018). Early prediction of chronic kidney disease using machine learning supported by predictive analytics. In: 2018 IEEE congress on evolutionary computation (CEC). IEEE, New York, pp 1–9

  • Alloghani M, Al-Jumeily D, Hussain A, Liatsis P, Aljaaf AJ (2020) Performance-based prediction of chronic kidney disease using machine learning for high-risk cardiovascular disease patients. Nature-inspired computation in data mining and machine learning. Springer, Cham, pp 187–206

    Google Scholar 

  • Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J et al (2019) Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med 109:101–111

    Google Scholar 

  • Almasoud M, Ward TE (2019) Detection of chronic kidney disease using machine learning algorithms with least number of predictors. Int J Soft Comput Appl. https://doi.org/10.14569/IJACSA.2019.0100813

    Article  Google Scholar 

  • Amdur RL, Chawla LS, Amodeo S, Kimmel PL, Palant CE (2009) Outcomes following diagnosis of acute renal failure in US veterans: focus on acute tubular necrosis. Kidney Int 76(10):1089–1097

    Google Scholar 

  • Arai H, Maung C, Xu K, Schweitzer H (2016). nsupervised feature selection by heuristic search with provable bounds on suboptimality. In: Proceedings of the AAAI conference on artificial intelligence, vol. 30, No. 1.

  • Basar MD, Akan A (2017) Detection of chronic kidney disease by using ensemble classifiers. In: 2017 10th international conference on electrical and electronics engineering (ELECO). IEEE, New York, pp 544–547

  • Bashir S, Khan ZS, Khan FH, Anjum A, Bashir K (2019). Improving heart disease prediction using feature selection approaches. In: 2019 16th international bhurban conference on applied sciences and technology (IBCAST). IEEE, New York, pp 619–623

  • Besra B, Majhi B (2019) An analysis on chronic kidney disease prediction system: cleaning, preprocessing, and effective classification of data. Recent findings in intelligent computing techniques. Springer, Singapore, pp 473–480

    Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    MATH  Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees, vol 432. Wadsworth International Group, Belmont, pp 151–166

    MATH  Google Scholar 

  • Briscoe E, Feldman J (2011) Conceptual complexity and the bias/variance tradeoff. Cognition 118(1):2–16

    Google Scholar 

  • Cai Q, Mukku KV, Ahmad M (2013) Coronary artery disease in patients with chronic kidney disease: a clinical update. Curr Cardiol Rev 9(4):331–339

    Google Scholar 

  • Chalak LF, Pavageau L, Huet B, Hynan L (2020) Statistical rigor and kappa considerations: which, when and clinical context matters. Pediatr Res 88(1):5–5

    Google Scholar 

  • Charleonnan A, Fufaung T, Niyomwong T, Chokchueypattanakit W, Suwannawach S, Ninchawee N (2016). Predictive analytics for chronic kidney disease using machine learning techniques. In: 2016 management and innovation technology international conference (MITicon). IEEE, New York, pp MIT-80

  • Chatterjee S, Banerjee S, Basu P, Debnath M, Sen S (2017) Cuckoo search coupled artificial neural network in detection of chronic kidney disease. In: 2017 1st international conference on electronics, materials engineering and nano-technology (IEMENTech). IEEE, New York, pp 1–4

  • Chawla LS, Kimmel PL (2012) Acute kidney injury and chronic kidney disease: an integrated clinical syndrome. Kidney Int 82(5):516–524

    Google Scholar 

  • Chawla LS, Amdur RL, Amodeo S, Kimmel PL, Palant CE (2011) The severity of acute kidney injury predicts progression to chronic kidney disease. Kidney Int 79(12):1361–1369

    Google Scholar 

  • Chawla LS, Eggers PW, Star RA, Kimmel PL (2014) Acute kidney injury and chronic kidney disease as interconnected syndromes. N Engl J Med 371(1):58–66

    Google Scholar 

  • Chen Z, Zhang Z, Zhu R, Xiang Y, Harrington PB (2016) Diagnosis of patients with chronic kidney disease by using two fuzzy classifiers. Chemom Intell Lab Syst 153:140–145

    Google Scholar 

  • Chetty N, Vaisla KS, Sudarsan SD (2015) Role of attributes selection in classification of Chronic Kidney Disease patients. In: 2015 international conference on computing, communication and security (ICCCS). IEEE, New York, pp 1–6

  • Chronic Kidney Disease Prognosis Consortium (2010) Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 375(9731):2073–2081

    Google Scholar 

  • Chung CJ, Kuo YC, Hsieh YY, Li TC, Lin CC, Liang WM et al (2017) Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics. Int J Med Inf 107:18–29

    Google Scholar 

  • Coca SG, Singanamala S, Parikh CR (2012) Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int 81(5):442–448

    Google Scholar 

  • Coresh J, Wei GL, McQuillan G, Brancati FL, Levey AS, Jones C, Klag MJ (2001) Prevalence of high blood pressure and elevated serum creatinine level in the United States: findings from the third National Health and Nutrition Examination Survey (1988–1994). Arch Intern Med 161(9):1207–1216

    Google Scholar 

  • Davazdahemami B, Delen D (2019) The confounding role of common diabetes medications in developing acute renal failure: a data mining approach with emphasis on drug-drug interactions. Expert Syst Appl 123:168–177

    Google Scholar 

  • de Barros RSM, Hidalgo JIG, de Lima Cabral DR (2018) Wilcoxon rank sum test drift detector. Neurocomputing 275:1954–1963

  • Devika R, Avilala SV, Subramaniyaswamy V (2019) Comparative study of classifier for chronic kidney disease prediction using Naive Bayes, KNN and random forest. In: 2019 3rd international conference on computing methodologies and communication (ICCMC). IEEE, New York, pp 679–684

  • Di Noia T, Ostuni VC, Pesce F, Binetti G, Naso D, Schena FP, Di Sciascio E (2013) An end stage kidney disease predictor based on an artificial neural networks ensemble. Expert Syst Appl 40(11):4438–4445

    Google Scholar 

  • Dolatabadi AD, Khadem SEZ, Asl BM (2017) Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput Methods Programs Biomed 138:117–126

    Google Scholar 

  • Draper NR, Smith H (1998) Applied regression analysis, vol 326. John Wiley & Sons, Hoboken

    MATH  Google Scholar 

  • Dubey A (2015) A classification of ckd cases using multivariate k-means clustering. Int J Sci Res Publ 5(8):1–5

    Google Scholar 

  • Elhoseny M, Shankar K, Uthayakumar J (2019) Intelligent diagnostic prediction and classification system for chronic kidney disease. Sci Rep 9(1):1–14

    Google Scholar 

  • Escanilla NS, Hellerstein L, Kleiman R, Kuang Z, Shull J, Page D (2018). Recursive feature elimination by sensitivity testing. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA). IEEE, New York, pp 40–47

  • Fan J, Upadhye S, Worster A (2006) Understanding receiver operating characteristic (ROC) curves. Can J Emerg Med 8(1):19–20

    Google Scholar 

  • Gansevoort RT, Matsushita K, Van Der Velde M, Astor BC, Woodward M, Levey AS et al (2011) Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int 80(1):93–104

    Google Scholar 

  • Giovannetti S, Barsotti G (1991) defense of creatinine clearance. Nephron 59(1):11–14

    Google Scholar 

  • Golberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addion wesley 1989(102):36

    Google Scholar 

  • Goldstein SL (2012) Acute kidney injury in children and its potential consequences in adulthood. Blood Purif 33(1–3):131–137

    Google Scholar 

  • Gunasundari S, Janakiraman S, Meenambal S (2016) Velocity bounded boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis. Expert Syst Appl 56:28–47

    Google Scholar 

  • Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389–422

    MATH  Google Scholar 

  • Hasan KZ, Hasan MZ (2019) Performance evaluation of ensemble-based machine learning techniques for prediction of chronic kidney disease. Emerging research in computing, information, communication and applications. Springer, Singapore, pp 415–426

    Google Scholar 

  • Hore S, Chatterjee S, Shaw RK, Dey N, Virmani J (2018) Detection of chronic kidney disease: a NN-GA-based approach. Nature Inspired Computing. Springer, Singapore, pp 109–115

    Google Scholar 

  • Ishani A, Xue JL, Himmelfarb J, Eggers PW, Kimmel PL, Molitoris BA, Collins AJ (2009) Acute kidney injury increases risk of ESRD among elderly. J Am Soc Nephrol 20(1):223–228

    Google Scholar 

  • Ishani A, Nelson D, Clothier B, Schult T, Nugent S, Greer N et al (2011) The magnitude of acute serum creatinine increase after cardiac surgery and the risk of chronic kidney disease, progression of kidney disease, and death. Arch Intern Med 171(3):226–233

    Google Scholar 

  • James MT, Hemmelgarn BR, Wiebe N, Pannu N, Manns BJ, Klarenbach SW et al (2010) Glomerular filtration rate, proteinuria, and the incidence and consequences of acute kidney injury: a cohort study. Lancet 376(9758):2096–2103

    Google Scholar 

  • Jerlin Rubini L, Perumal E (2020) Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm. Int J Imaging Syst Technol 30(3):660–673

    Google Scholar 

  • Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B et al (2013) Chronic kidney disease: global dimension and perspectives. Lancet 382(9888):260–272

    Google Scholar 

  • Kemal ADEM (2018) Diagnosis of chronic kidney disease using random subspace method with particle swarm optimization. Int J Eng Res Dev 10(3):1–5

    Google Scholar 

  • Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324

    MATH  Google Scholar 

  • Kopple JD (2001) The National Kidney Foundation K/DOQI clinical practice guidelines for dietary protein intake for chronic dialysis patients. Am J Kidney Dis 38(4):S68–S73

    Google Scholar 

  • Kriplani H, Patel B, Roy S (2019) Prediction of chronic kidney diseases using deep artificial neural network technique. Computer aided intervention and diagnostics in clinical and medical images. Springer, Cham, pp 179–187

    Google Scholar 

  • Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59(1–2):161–205

    MATH  Google Scholar 

  • Larson R, Farber E, Farber E (2009) Elementary statistics: picturing the world. Pearson Prentice Hall

  • Lee S, Schowe B, Sivakumar V, Morik K (2012) Feature selection for high-dimensional data with rapidminer. Universitätsbibliothek Dortmund

  • Levey AS, Coresh J (2012) Chronic kidney disease. Lancet 379(9811):165–180

    Google Scholar 

  • Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 130(6):461–470

    Google Scholar 

  • Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU et al (2007) Chronic kidney disease as a global public health problem: approaches and initiatives—a position statement from Kidney Disease Improving Global Outcomes. Kidney Int 72(3):247–259

    Google Scholar 

  • Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro AF III, Feldman HI et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604–612

    Google Scholar 

  • Levin A, Hemmelgarn B, Culleton B, Tobe S, McFarlane P, Ruzicka M et al (2008) Guidelines for the management of chronic kidney disease. CMAJ 179(11):1154–1162

    Google Scholar 

  • Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2017) Feature selection: a data perspective. ACM Computing Surveys (CSUR) 50(6):1–45

    Google Scholar 

  • Malmir B, Amini M, Chang SI (2017) A medical decision support system for disease diagnosis under uncertainty. Expert Syst Appl 88:95–108

    Google Scholar 

  • Manikandan R, Patan R, Gandomi AH, Sivanesan P, Kalyanaraman H (2020) Hash polynomial two factor decision tree using IoT for smart health care scheduling. Expert Syst Appl 141:112924

    Google Scholar 

  • McRae MP, Bozkurt B, Ballantyne CM, Sanchez X, Christodoulides N, Simmons G et al (2016) Cardiac ScoreCard: a diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease. Expert Syst Appl 54:136–147

    Google Scholar 

  • Meza-Palacios R, Aguilar-Lasserre AA, Ureña-Bogarín EL, Vázquez-Rodríguez CF, Posada-Gómez R, Trujillo-Mata A (2017) Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus. Expert Syst Appl 72:335–343

    Google Scholar 

  • Mitchell TM (2006) The discipline of machine learning, vol 9. Carnegie Mellon University, School of Computer Science, Machine Learning Department, Pittsburgh

    Google Scholar 

  • Mohammed Siyad B, Manoj M, Mohammed Siyad B, Manoj M (2016) Fused features classification for the effective prediction of chronic kidney disease. Int J 2:44–48

    Google Scholar 

  • Nadi A, Moradi H (2019) Increasing the views and reducing the depth in random forest. Expert Syst Appl 138:112801

    Google Scholar 

  • Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Comput Archit Lett 26(09):917–922

    MATH  Google Scholar 

  • Neter J, Wasserman W, Kutner MH (1990) Applied linear statistical models: regression, analysis of variance, and experimental designs. Richard D Irwin, Homewood

    Google Scholar 

  • Nilashi M, Roudbaraki MZ, Farahmand M (2017) A Predictive method for mesothelioma disease classification using Naïve Bayes classifier. J Soft Comput Decis Support Syst 4(6):7–14

    Google Scholar 

  • Nilashi M, Ahmadi H, Sheikhtaheri A, Naemi R, Alotaibi R, Alarood AA et al (2020) Remote tracking of parkinson’s disease progression using ensembles of deep belief network and self-organizing map. Expert Syst Appl 159:113562

    Google Scholar 

  • Perrone RD, Madias NE, Levey AS (1992) Serum creatinine as an index of renal function: new insights into old concepts. Clin Chem 38(10):1933–1953

    Google Scholar 

  • Qin J, Chen L, Liu Y, Liu C, Feng C, Chen B (2019) A machine learning methodology for diagnosing chronic kidney disease. IEEE Access 8:20991–21002

    Google Scholar 

  • Radha N, Ramya S (2015) Performance analysis of machine learning algorithms for predicting chronic kidney disease. Int J Comput Sci Eng Open Access 3:72–76

    Google Scholar 

  • Raghavendra U, Fujita H, Gudigar A, Shetty R, Nayak K, Pai U et al (2018) Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images. Biomed Signal Process Control 40:324–334

    Google Scholar 

  • Ray A, Chaudhuri AK (2021) Smart healthcare disease diagnosis and patient management: innovation, improvement and skill development. Mach Learn Appl 3:100011

    Google Scholar 

  • Rubini LJ (2015) UCIMachineLearningRepository. Karaikudi. TamilNadu: Algappa University, Department of Computer Science and Engineering. http://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease.

  • Salekin A, Stankovic J (2016). Detection of chronic kidney disease and selecting important predictive attributes. In: 2016 IEEE international conference on healthcare informatics (ICHI). IEEE, New York, pp 262–270

  • Saringat Z, Mustapha A, Saedudin RR, Samsudin NA (2019) Comparative analysis of classification algorithms for chronic kidney disease diagnosis. Bull Electr Eng Inf 8(4):1496–1501

    Google Scholar 

  • Schreiner SJ, Imbach LL, Werth E, Poryazova R, Baumann-Vogel H, Valko PO et al (2019) Slow-wave sleep and motor progression in Parkinson disease. Ann Neurol 85(5):765–770

    Google Scholar 

  • Sharaff A, Gupta H (2019) Extra-tree classifier with metaheuristics approach for email classification. Advances in computer communication and computational sciences. Springer, Singapore, pp 189–197

    Google Scholar 

  • Sinha P, Sinha P (2015) Comparative study of chronic kidney disease prediction using KNN and SVM. Int J Eng Res Technol 4(12):608–612

    Google Scholar 

  • Speiser JL, Miller ME, Tooze J, Ip E (2019) A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst Appl 134:93–101

    Google Scholar 

  • Stevens LA, Levey AS (2009) Current status and future perspectives for CKD testing. Am J Kidney Dis 53(3):S17–S26

    Google Scholar 

  • Tazin N, Sabab SA, Chowdhury MT (2016) Diagnosis of Chronic Kidney Disease using effective classification and feature selection technique. In: 2016 international conference on medical engineering, health informatics and technology (MediTec). IEEE, New York, pp 1–6

  • Thakar CV, Christianson A, Himmelfarb J, Leonard AC (2011) Acute kidney injury episodes and chronic kidney disease risk in diabetes mellitus. Clin J Am Soc Nephrol 6(11):2567–2572

    Google Scholar 

  • Tikariha P, Richhariya P (2018) Comparative study of chronic kidney disease prediction using different classification techniques. In: Proceedings of international conference on recent advancement on computer and communication. Springer, Singapore, pp 195–203

  • Vandewiele G, Dehaene I, Kovács G, Sterckx L, Janssens O, Ongenae F, VanHoecke S (2020) Overly optimistic prediction results on imbalanced data: flaws and benefits of applying over-sampling. Preprint at https://arxiv.org/abs/quant-ph/2001.06296

  • Wahba G, Wang Y, Gu C, Klein R, Klein B (1994) Structured machine learning forsoft’classification with smoothing spline ANOVA and stacked tuning, testing and evaluation. Adv Neural Inf Process Syst 6:415–422

    Google Scholar 

  • Wahba G, Lin X, Gao F, Xiang D, Klein R, Klein BE (1998). The bias-variance tradeoff and the randomized GACV. In: NIPS, pp 620–626

  • Wald R, Quinn RR, Luo J, Li P, Scales DC, Mamdani MM et al (2009) Chronic dialysis and death among survivors of acute kidney injury requiring dialysis. JAMA 302(11):1179–1185

    Google Scholar 

  • Weiss SM, Kulikowski CA (1991) Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. Morgan Kaufmann Publishers Inc., Burlington

    Google Scholar 

  • Wibawa MS, Maysanjaya IMD, Putra IMAW (2017) Boosted classifier and features selection for enhancing chronic kidney disease diagnose. In: 2017 5th international conference on cyber and IT service management (CITSM). IEEE, New York, pp 1–6

  • Wilcoxon F (1992) Individual comparisons by ranking methods. Breakthroughs in statistics. Springer, New York, pp 196–202

    Google Scholar 

  • World Health Organization (2011) Global status report on noncommunicable diseases 2010. WHO, Geneva

    Google Scholar 

  • Zeynu S, Patil S (2018) Prediction of chronic kidney disease using data mining feature selection and ensemble method. Int J Data Min Genomics Proteomics 9(1):1–9

    Google Scholar 

  • Zhang Y, Wang S, Phillips P, Ji G (2014) Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl-Based Syst 64:22–31

    Google Scholar 

Download references

Acknowledgements

The authors thank the anonymous referees, and the editor for their valuable feedback, which significantly improved the positioning and presentation of this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Techno Engineering College Banipur, Kolkata, India

    Avijit Kumar Chaudhuri

  2. Indian Institute of Foreign Trade, 1583 Madurdaha, Chowbagha Road, Ward No 108, Borough XII, Kolkata, 700107, India

    Deepankar Sinha

  3. Seacom Skills University, Kendradangal, Birbhum, Bolpur, West Bengal, 731236, India

    Dilip K. Banerjee

  4. University of Engineering & Management, Kolkata, India

    Anirban Das

Authors
  1. Avijit Kumar Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepankar Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dilip K. Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anirban Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avijit Kumar Chaudhuri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaudhuri, A.K., Sinha, D., Banerjee, D.K. et al. A novel enhanced decision tree model for detecting chronic kidney disease. Netw Model Anal Health Inform Bioinforma 10, 29 (2021). https://doi.org/10.1007/s13721-021-00302-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-021-00302-w

Keywords

Search

Navigation