This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2 (4), 230–243. https://doi.org/10.1136/svn-2017-000101JiangF.JiangY.ZhiH.DongY.LiH.MaS.WangY.DongQ.ShenH.WangY.2017Artificial intelligence in healthcare: Past, present and futureStroke and Vascular Neurology24230243https://doi.org/10.1136/svn-2017-000101Search in Google Scholar
Chai, Y., Li, J., Sun, X., Lei, Z., Zhang, Z., Hou, G. (2020). The performance of a body composition–based equation in estimating overhydration of hemodialysis patients. SN Comprehensive Clinical Medicine, 2 (7), 909–913. https://doi.org/10.1007/s42399-020-00338-5ChaiY.LiJ.SunX.LeiZ.ZhangZ.HouG.2020The performance of a body composition–based equation in estimating overhydration of hemodialysis patientsSN Comprehensive Clinical Medicine27909913https://doi.org/10.1007/s42399-020-00338-5Search in Google Scholar
Pérez-Morales, R., Donate-Correa, J., Martín-Núñez, E., Pérez-Delgado, N., Ferri, C., López-Montes, A., Jiménez-Sosa, A., Navarro-González, J. F. (2021). Extracellular water/total body water ratio as predictor of mortality in hemodialysis patients. Renal Failure, 43 (1), 821–829. https://doi.org/10.1080/0886022x.2021.1922442Pérez-MoralesR.Donate-CorreaJ.Martín-NúñezE.Pérez-DelgadoN.FerriC.López-MontesA.Jiménez-SosaA.Navarro-GonzálezJ. F.2021Extracellular water/total body water ratio as predictor of mortality in hemodialysis patientsRenal Failure431821829https://doi.org/10.1080/0886022x.2021.1922442Search in Google Scholar
Nakagawa, H., Sato, Y., Toshimori, H., Fujimoto, S. (2015). Evaluation of a new bio-impedance spectroscopy device in healthy Japanese. Bio-Medical Materials and Engineering, 26 (3-4), 97–102. https://doi.org/10.3233/bme-151553NakagawaH.SatoY.ToshimoriH.FujimotoS.2015Evaluation of a new bio-impedance spectroscopy device in healthy JapaneseBio-Medical Materials and Engineering263-497102https://doi.org/10.3233/bme-151553Search in Google Scholar
Loutradis, C., Sarafidis, P. A., Ekart, R., Papadopoulos, C., Sachpekidis, V., Alexandrou, M. E., Papadopoulou, D., Efstratiadis, G., Papagianni, A., London, G., Zoccali, C. (2019). The effect of dry-weight reduction guided by lung ultrasound on ambulatory blood pressure in hemodialysis patients: A randomized controlled trial. Kidney International, 95 (6), 1505–1513. https://doi.org/10.1016/j.kint.2019.02.018LoutradisC.SarafidisP. A.EkartR.PapadopoulosC.SachpekidisV.AlexandrouM. E.PapadopoulouD.EfstratiadisG.PapagianniA.LondonG.ZoccaliC.2019The effect of dry-weight reduction guided by lung ultrasound on ambulatory blood pressure in hemodialysis patients: A randomized controlled trialKidney International95615051513https://doi.org/10.1016/j.kint.2019.02.018Search in Google Scholar
Yajima, T., Arao, M., Yajima, K., Takahashi, H., Yasuda, K. (2019). The associations of fat tissue and muscle mass indices with all-cause mortality in patients undergoing hemodialysis. PloS One, 14 (2), e0211988. https://doi.org/10.1371/journal.pone.0211988YajimaT.AraoM.YajimaK.TakahashiH.YasudaK.2019The associations of fat tissue and muscle mass indices with all-cause mortality in patients undergoing hemodialysisPloS One142e0211988https://doi.org/10.1371/journal.pone.0211988Search in Google Scholar
Zaloszyc, A., Schaefer, B., Schaefer, F., Krid, S., Salomon, R., Niaudet, P., Schmitt, C. P., Fischbach, M. (2013). Hydration measurement by bioimpedance spectroscopy and blood pressure management in children on hemodialysis. Pediatric Nephrology, 28 (11), 2169–2177. https://doi.org/10.1007/s00467-013-2540-6ZaloszycA.SchaeferB.SchaeferF.KridS.SalomonR.NiaudetP.SchmittC. P.FischbachM.2013Hydration measurement by bioimpedance spectroscopy and blood pressure management in children on hemodialysisPediatric Nephrology281121692177https://doi.org/10.1007/s00467-013-2540-6Search in Google Scholar
Rymarz, A., Gibińska, J., Zajbt, M., Piechota, W., Niemczyk, S. (2018). Low lean tissue mass can be a predictor of one-year survival in hemodialysis patients. Renal Failure, 40 (1), 231–237. https://doi.org/10.1080/0886022x.2018.1456451RymarzA.GibińskaJ.ZajbtM.PiechotaW.NiemczykS.2018Low lean tissue mass can be a predictor of one-year survival in hemodialysis patientsRenal Failure401231237https://doi.org/10.1080/0886022x.2018.1456451Search in Google Scholar
Marcelli, D., Usvyat, L. A., Kotanko, P., Bayh, I., Canaud, B., Etter, M., Gatti, E., Grassmann, A., Wang, Y., Marelli, C., Scatizzi, L., Stopper, A., van der Sande, F. M., Kooman, J. (2015). Body composition and survival in dialysis patients: Results from an international cohort study. Clinical Journal of the American Society of Nephrology, 10 (7), 1192–1200. https://doi.org/10.2215/cjn.08550814MarcelliD.UsvyatL. A.KotankoP.BayhI.CanaudB.EtterM.GattiE.GrassmannA.WangY.MarelliC.ScatizziL.StopperA.van der SandeF. M.KoomanJ.2015Body composition and survival in dialysis patients: Results from an international cohort studyClinical Journal of the American Society of Nephrology10711921200https://doi.org/10.2215/cjn.08550814Search in Google Scholar
Khalil, S. F., Mohktar, M. S., Ibrahim, F. (2014). The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases. Sensors, 14 (6), 10895–10928. https://doi.org/10.3390/s140610895KhalilS. F.MohktarM. S.IbrahimF.2014The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseasesSensors1461089510928https://doi.org/10.3390/s140610895Search in Google Scholar
Jaffrin, M. Y., Morel, H. (2008). Body fluid volumes measurements by impedance: A review of bioimpedance spectroscopy (BIS) and bioimpedance analysis (BIA) methods. Medical Engineering & Physics, 30 (10), 1257–1269. https://doi.org/10.1016/j.medengphy.2008.06.009JaffrinM. Y.MorelH.2008Body fluid volumes measurements by impedance: A review of bioimpedance spectroscopy (BIS) and bioimpedance analysis (BIA) methodsMedical Engineering & Physics301012571269https://doi.org/10.1016/j.medengphy.2008.06.009Search in Google Scholar
Cole, K. S. (1940). Permeability and impermeability of cell membranes for ions. Cold Spring Harbor Symposia on Quantitative Biology, 8, 110–122. https://doi.org/10.1101/SQB.1940.008.01.013ColeK. S.1940Permeability and impermeability of cell membranes for ionsCold Spring Harbor Symposia on Quantitative Biology8110122https://doi.org/10.1101/SQB.1940.008.01.013Search in Google Scholar
Chamney, P. W., Wabel, P., Moissl, U. M., Müller, M. J., Bosy-Westphal, A., Korth, O., Fuller, N. J. (2007). A whole-body model to distinguish excess fluid from the hydration of major body tissues. The American Journal of Clinical Nutrition, 85 (1), 80–89. https://doi.org/10.1093/ajcn/85.1.80ChamneyP. W.WabelP.MoisslU. M.MüllerM. J.Bosy-WestphalA.KorthO.FullerN. J.2007A whole-body model to distinguish excess fluid from the hydration of major body tissuesThe American Journal of Clinical Nutrition8518089https://doi.org/10.1093/ajcn/85.1.80Search in Google Scholar
Chamney, P. W., Krämer, M., Rode, C., Kleinekofort, W., Wizemann, V. (2002). A new technique for establishing dry weight in hemodialysis patients via whole body bioimpedance. Kidney International, 61 (6), 2250–2258. https://doi.org/10.1046/j.1523-1755.2002.00377.xChamneyP. W.KrämerM.RodeC.KleinekofortW.WizemannV.2002A new technique for establishing dry weight in hemodialysis patients via whole body bioimpedanceKidney International61622502258https://doi.org/10.1046/j.1523-1755.2002.00377.xSearch in Google Scholar
Zoccali, C., Tripepi, R., Torino, C., Bellantoni, M., Tripepi, G., Mallamaci, F. (2014). Lung congestion as a risk factor in end-stage renal disease. Blood Purification, 36 (3-4), 184–191. https://doi.org/10.1159/000356085ZoccaliC.TripepiR.TorinoC.BellantoniM.TripepiG.MallamaciF.2014Lung congestion as a risk factor in end-stage renal diseaseBlood Purification363-4184191https://doi.org/10.1159/000356085Search in Google Scholar
Agarwal, R., Weir, M. R. (2010). Dry-weight: A concept revisited in an effort to avoid medication-directed approaches for blood pressure control in hemodialysis patients. Clinical Journal of the American Society of Nephrology, 5 (7), 1255–1260. https://doi.org/10.2215/cjn.01760210AgarwalR.WeirM. R.2010Dry-weight: A concept revisited in an effort to avoid medication-directed approaches for blood pressure control in hemodialysis patientsClinical Journal of the American Society of Nephrology5712551260https://doi.org/10.2215/cjn.01760210Search in Google Scholar
Agarwal, R., Kelley, K., Light, R. P. (2008). Diagnostic utility of blood volume monitoring in hemodialysis patients. American Journal of Kidney Diseases, 51 (2), 242–254. https://doi.org/10.1053/j.ajkd.2007.10.036AgarwalR.KelleyK.LightR. P.2008Diagnostic utility of blood volume monitoring in hemodialysis patientsAmerican Journal of Kidney Diseases512242254https://doi.org/10.1053/j.ajkd.2007.10.036Search in Google Scholar
Wizemann, V., Schilling, M. (1995). Dilemma of assessing volume state—the use and the limitations of a clinical score. Nephrology Dialysis Transplantation, 10 (11), 2114–2117. https://doi.org/10.1093/ndt/10.11.2114WizemannV.SchillingM.1995Dilemma of assessing volume state—the use and the limitations of a clinical scoreNephrology Dialysis Transplantation101121142117https://doi.org/10.1093/ndt/10.11.2114Search in Google Scholar
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21 (1), 125. https://doi.org/10.1186/s12911-021-01488-9SecinaroS.CalandraD.SecinaroA.MuthuranguV.BianconeP.2021The role of artificial intelligence in healthcare: A structured literature reviewBMC Medical Informatics and Decision Making211125https://doi.org/10.1186/s12911-021-01488-9Search in Google Scholar
Kanda, E., Epureanu, B. I., Adachi, T., Tsuruta, Y., Kikuchi, K., Kashihara, N., Abe, M., Masakane, I., Nitta, K. (2020). Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan. PloS One, 15 (5), e0233491. https://doi.org/10.1371/journal.pone.0233491KandaE.EpureanuB. I.AdachiT.TsurutaY.KikuchiK.KashiharaN.AbeM.MasakaneI.NittaK.2020Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in JapanPloS One155e0233491https://doi.org/10.1371/journal.pone.0233491Search in Google Scholar
Guo, X., Zhou, W., Lu, Q., Du, A., Cai, Y., Ding, Y. (2021). Assessing dry weight of hemodialysis patients via Sparse Laplacian regularized RVFL neural network with L2,1-norm. BioMed Research International, 2021, 627650. https://doi.org/10.1155/2021/6627650GuoX.ZhouW.LuQ.DuA.CaiY.DingY.2021Assessing dry weight of hemodialysis patients via Sparse Laplacian regularized RVFL neural network with L2,1-normBioMed Research International2021627650https://doi.org/10.1155/2021/6627650Search in Google Scholar
Djordjevic, S., Kostic, M., Milosevic, D., Cvetkovic, M., Mitrovic, K., Mladenovic, V. (2023). Prediction of overhydration in the process of pediatric hemodialysis using artificial neural network. In 2023 12th Mediterranean Conference on Embedded Computing (MECO). IEEE. https://doi.org/10.1109/MECO58584.2023.10154915DjordjevicS.KosticM.MilosevicD.CvetkovicM.MitrovicK.MladenovicV.2023Prediction of overhydration in the process of pediatric hemodialysis using artificial neural networkIn2023 12th Mediterranean Conference on Embedded Computing (MECO)IEEEhttps://doi.org/10.1109/MECO58584.2023.10154915Search in Google Scholar
Muntasir Nishat, M., Faisal, F., Rahman Dip, R., Nasrullah, S. M., Ahsan, R., Shikder, F., Ar-Raihan Asif, M. A., Hoque, M. A. (2021). A comprehensive analysis on detecting chronic kidney disease by employing machine learning algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 7 (29), 170671. https://doi.org/10.4108/eai.13-8-2021.170671Muntasir NishatM.FaisalF.Rahman DipR.NasrullahS. M.AhsanR.ShikderF.Ar-Raihan AsifM. A.HoqueM. A.2021A comprehensive analysis on detecting chronic kidney disease by employing machine learning algorithmsEAI Endorsed Transactions on Pervasive Health and Technology729170671https://doi.org/10.4108/eai.13-8-2021.170671Search in Google Scholar
Belur Nagaraj, S., Pena, M. J., Ju, W., Heerspink, H. L., The BEAt-DKD Consortium. (2020). Machine-learning–based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data. Diabetes, Obesity & Metabolism, 22 (12), 2479–2486. https://doi.org/10.1111/dom.14178Belur NagarajS.PenaM. J.JuW.HeerspinkH. L.The BEAt-DKD Consortium2020Machine-learning–based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials dataDiabetes, Obesity & Metabolism221224792486https://doi.org/10.1111/dom.14178Search in Google Scholar
Arif, M. S., Mukheimer, A., Asif, D. (2023). Enhancing the early detection of chronic kidney disease: A robust machine learning model. Big Data and Cognitive Computing, 7 (3), 144. https://doi.org/10.3390/bdcc7030144ArifM. S.MukheimerA.AsifD.2023Enhancing the early detection of chronic kidney disease: A robust machine learning modelBig Data and Cognitive Computing73144https://doi.org/10.3390/bdcc7030144Search in Google Scholar
Swain, D., Mehta, U., Bhatt, A., Patel, H., Patel, K., Mehta, D., Acharya, B., Gerogiannis, V. C., Kanavos, A., Manika, S. (2023). A robust chronic kidney disease classifier using machine learning. Electronics, 12 (1), 212. https://doi.org/10.3390/electronics12010212SwainD.MehtaU.BhattA.PatelH.PatelK.MehtaD.AcharyaB.GerogiannisV. C.KanavosA.ManikaS.2023A robust chronic kidney disease classifier using machine learningElectronics121212https://doi.org/10.3390/electronics12010212Search in Google Scholar
Shih, C.-C., Chen, S.-H., Chen, G.-D., Chang, C.-C., Shih, Y.-L. (2021). Development of a longitudinal diagnosis and prognosis in patients with chronic kidney disease: Intelligent clinical decision-making scheme. International Journal of Environmental Research and Public Health, 18 (23), 12807. https://doi.org/10.3390/ijerph182312807ShihC.-C.ChenS.-H.ChenG.-D.ChangC.-C.ShihY.-L.2021Development of a longitudinal diagnosis and prognosis in patients with chronic kidney disease: Intelligent clinical decision-making schemeInternational Journal of Environmental Research and Public Health182312807https://doi.org/10.3390/ijerph182312807Search in Google Scholar
Swain, D., Patel, H., Patel, K., Sakariya, V., Chaudhari, N. (2022). An intelligent clinical support system for the early diagnosis of the chronic kidney disease. In 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). IEEE. https://doi.org/10.1109/iSSSC56467.2022.10051517SwainD.PatelH.PatelK.SakariyaV.ChaudhariN.2022An intelligent clinical support system for the early diagnosis of the chronic kidney diseaseIn2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)IEEEhttps://doi.org/10.1109/iSSSC56467.2022.10051517Search in Google Scholar
Mladenović, V., Kostić, M., Milošević, D., Zanaj, E., Đorđević, S. (2024). System for prediction and balancing excess fluid in the body during hemodialysis based on artificial intelligence. Patent RS20240030A2.MladenovićV.KostićM.MiloševićD.ZanajE.ĐorđevićS.2024System for prediction and balancing excess fluid in the body during hemodialysis based on artificial intelligencePatent RS20240030A2.Search in Google Scholar
Ren, Y., Fei, H., Liang, X., Ji, D., Cheng, M. (2019). A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records. BMC Medical Informatics and Decision Making, 19 (S2), 51. https://doi.org/10.1186/s12911-019-0765-4RenY.FeiH.LiangX.JiD.ChengM.2019A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health recordsBMC Medical Informatics and Decision Making19S251https://doi.org/10.1186/s12911-019-0765-4Search in Google Scholar
Singh, V., Jain, D. (2021). A hybrid parallel classification model for the diagnosis of chronic kidney disease. International Journal of Interactive Multimedia and Artificial Intelligence, 8 (2). https://doi.org/10.9781/ijimai.2021.10.008SinghV.JainD.2021A hybrid parallel classification model for the diagnosis of chronic kidney diseaseInternational Journal of Interactive Multimedia and Artificial Intelligence82https://doi.org/10.9781/ijimai.2021.10.008Search in Google Scholar
Dey, S. K., Uddin, K. M. M., Babu, H. M. H., Rahman, M. M., Howlader, A., Uddin, K. M. A. (2022). Chi2-MI: A hybrid feature selection based machine learning approach in diagnosis of chronic kidney disease. Intelligent Systems with Applications, 16, 200144. https://doi.org/10.1016/j.iswa.2022.200144DeyS. K.UddinK. M. M.BabuH. M. H.RahmanM. M.HowladerA.UddinK. M. A.2022Chi2-MI: A hybrid feature selection based machine learning approach in diagnosis of chronic kidney diseaseIntelligent Systems with Applications16200144https://doi.org/10.1016/j.iswa.2022.200144Search in Google Scholar
Khalid, H., Khan, A., Khan, M. Z., Mehmood, G., Qureshi, M. S. (2023). Machine learning hybrid model for the prediction of chronic kidney disease. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2023/9266889KhalidH.KhanA.KhanM. Z.MehmoodG.QureshiM. S.2023Machine learning hybrid model for the prediction of chronic kidney diseaseComputational Intelligence and Neurosciencehttps://doi.org/10.1155/2023/9266889Search in Google Scholar
Yadav, D. C., Pal, S. (2021). Performance based evaluation ofAlgorithmson Chronic Kidney Disease using hybrid ensemble model in machine learning. Biomedical & Pharmacology Journal, 14 (3), 1633–1645. https://dx.doi.org/10.13005/bpj/2264YadavD. C.PalS.2021Performance based evaluation ofAlgorithmson Chronic Kidney Disease using hybrid ensemble model in machine learningBiomedical & Pharmacology Journal14316331645https://dx.doi.org/10.13005/bpj/2264Search in Google Scholar
Ratnababu, M., Naidu, M. R. (2019). A hybrid method for detection of kidney disease using Machine Learning. Turkish Journal of Computer and Mathematics Education, 11 (2), 654–669.RatnababuM.NaiduM. R.2019A hybrid method for detection of kidney disease using Machine LearningTurkish Journal of Computer and Mathematics Education112654669Search in Google Scholar
Jhou, M.-J., Chen, M.-S., Lee, T.-S., Yang, C.-T., Chiu, Y.-L., Lu, C.-J. (2022). A hybrid risk factor evaluation scheme for metabolic syndrome and stage 3 chronic kidney disease based on multiple machine learning techniques. Healthcare, 10 (12), 2496. https://doi.org/10.3390/healthcare10122496JhouM.-J.ChenM.-S.LeeT.-S.YangC.-T.ChiuY.-L.LuC.-J.2022A hybrid risk factor evaluation scheme for metabolic syndrome and stage 3 chronic kidney disease based on multiple machine learning techniquesHealthcare10122496https://doi.org/10.3390/healthcare10122496Search in Google Scholar
Guli, U. J., Malgwi, Y. M., Adamu, I., Ezekiel, P. (2023). Hybrid diagnostic model for kidney disease prediction using data mining techniques. African Journal of Advances in Science and Technology Research, 11 (1), 75–89.GuliU. J.MalgwiY. M.AdamuI.EzekielP.2023Hybrid diagnostic model for kidney disease prediction using data mining techniquesAfrican Journal of Advances in Science and Technology Research1117589Search in Google Scholar
Zhao, Y., Ogden, R. T., Reiss, P. T. (2012). Wavelet-based LASSO in functional linear regression. Journal of Computational and Graphical Statistics, 21 (3), 600–617. https://doi.org/10.1080/10618600.2012.679241ZhaoY.OgdenR. T.ReissP. T.2012Wavelet-based LASSO in functional linear regressionJournal of Computational and Graphical Statistics213600617https://doi.org/10.1080/10618600.2012.679241Search in Google Scholar
Rao, H., Shi, X., Rodrigue, A. K., Feng, J., Xia, Y., Elhoseny, M., Yuan, X., Gu, L. (2019). Feature selection based on artificial bee colony and gradient boosting decision tree. Applied Soft Computing, 74, 634–642. https://doi.org/10.1016/j.asoc.2018.10.036RaoH.ShiX.RodrigueA. K.FengJ.XiaY.ElhosenyM.YuanX.GuL.2019Feature selection based on artificial bee colony and gradient boosting decision treeApplied Soft Computing74634642https://doi.org/10.1016/j.asoc.2018.10.036Search in Google Scholar
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29 (5), 1189–1232. https://doi.org/10.1214/aos/1013203451FriedmanJ. H.2001Greedy function approximation: A gradient boosting machineThe Annals of Statistics29511891232https://doi.org/10.1214/aos/1013203451Search in Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research, 12 (85), 2825–2830. http://jmlr.org/papers/v12/pedregosa11a.htmlPedregosaF.VaroquauxG.GramfortA.MichelV.ThirionB.GriselO.BlondelM.MüllerA.NothmanJ.LouppeG.PrettenhoferP.WeissR.DubourgV.VanderplasJ.PassosA.CournapeauD.BrucherM.PerrotM.DuchesnayÉ.2011Scikit-learn: Machine Learning in PythonInJournal of Machine Learning Research128528252830http://jmlr.org/papers/v12/pedregosa11a.htmlSearch in Google Scholar
Brentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532–541. https://doi.org/10.1016/j.cam.2016.02.009BrentanB. M.LuvizottoE.Jr.HerreraM.IzquierdoJ.Pérez-GarcíaR.2017Hybrid regression model for near real-time urban water demand forecastingJournal of Computational and Applied Mathematics309532541https://doi.org/10.1016/j.cam.2016.02.009Search in Google Scholar
Anaconda Inc. (2024). Unleash AI innovation and value. https://www.anaconda.com/Anaconda Inc.2024Unleash AI innovation and valuehttps://www.anaconda.com/Search in Google Scholar
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R. Second Edition. Springer, ISBN 978-1071614174.JamesG.WittenD.HastieT.TibshiraniR.2021An Introduction to Statistical Learning: With Applications in RSecond EditionSpringerISBN 978-1071614174.Search in Google Scholar
Algamal, Z. Y., Lee, M. H. (2015). Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Systems with Applications, 42 (23), 9326–9332. https://doi.org/10.1016/j.eswa.2015.08.016AlgamalZ. Y.LeeM. H.2015Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classificationExpert Systems with Applications422393269332https://doi.org/10.1016/j.eswa.2015.08.016Search in Google Scholar
Brentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532–541. https://doi.org/10.1016/j.cam.2016.02.009BrentanB. M.LuvizottoE.Jr.HerreraM.IzquierdoJ.Pérez-GarcíaR.2017Hybrid regression model for near real-time urban water demand forecastingJournal of Computational and Applied Mathematics309532541https://doi.org/10.1016/j.cam.2016.02.009Search in Google Scholar
Chicco, D., Warrens, M. J., Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ: Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623ChiccoD.WarrensM. J.JurmanG.2021The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluationPeerJ: Computer Science7e623https://doi.org/10.7717/peerj-cs.623Search in Google Scholar
Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. Springer. https://doi.org/10.1007/978-0-387-84858-7HastieT.TibshiraniR.FriedmanJ.2009The Elements of Statistical Learning: Data Mining, Inference, and PredictionSecond EditionSpringerhttps://doi.org/10.1007/978-0-387-84858-7Search in Google Scholar
Lin, H. T., Lin, C. J. (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical Report, Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei City, Taiwan.LinH. T.LinC. J.2003A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methodsTechnical Report,Department of Computer Science and Information Engineering, National Taiwan Normal UniversityTaipei City, TaiwanSearch in Google Scholar