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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

An Efficient Multiple Kernel Support Vector Regression Model for Assessing Dry Weight of Hemodialysis Patients

Author(s): Xiaoyi Guo, Wei Zhou, Bin Shi, Xiaohua Wang*, Aiyan Du*, Yijie Ding, Jijun Tang and Fei Guo*

Volume 16, Issue 2, 2021

Published on: 14 June, 2020

Page: [284 - 293] Pages: 10

DOI: 10.2174/1574893615999200614172536

Price: $65

Abstract

Background: Dry Weight (DW) is the lowest weight after dialysis, and patients with lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches have been presented to assess the dry weight of hemodialysis patients. However, these traditional methods all depend on special instruments and professional technicians.

Objective: In order to avoid this limitation, we need to find a machine-independent way to assess dry weight, therefore we collected some clinical influencing characteristic data and constructed a Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients.

Methods: In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements, and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were closely related to their dry weight. All these relevant data were used to enter the regression equation. Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS) model was proposed to predict the dry weight of hemodialysis patients.

Results: The experimental results show that dry weight is positively correlated with BMI and HR. And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negatively correlated with dry weight. Moreover, the Root Mean Square Error (RMSE) of our model was 1.3817.

Conclusion: Our proposed model could serve as a viable alternative for dry weight estimation of hemodialysis patients, thus providing a new way for clinical practice.

Keywords: Dry weight, hemodialysis, machine learning, multiple kernel learning, support vector regression, blood pressure.

Graphical Abstract
[1]
Grassmann A, Uhlenbusch-Körwer I, Bonnie-Schorn E, et al. Composition and management of hemodialysis fluids. Pabst Science Publishers 2000.
[2]
Wabel P, Chamney P, Moissl U, Jirka T. Importance of whole-body bioimpedance spectroscopy for the management of fluid balance. Blood Purif 2009; 27(1): 75-80.
[http://dx.doi.org/10.1159/000167013] [PMID: 19169022]
[3]
Alexiadis G, Panagoutsos S, Roumeliotis S, et al. Comparison of multiple fluid status assessment methods in patients on chronic hemodialysis. Int Urol Nephrol 2017; 49(3): 525-32.
[http://dx.doi.org/10.1007/s11255-016-1473-y] [PMID: 27943170]
[4]
Ohashi Y, Sakai K, Hase H, Joki N. Dry weight targeting: The art and science of conventional hemodialysis. Semin Dial 2018; 31(6): 551-6.
[http://dx.doi.org/10.1111/sdi.12721] [PMID: 29876972]
[5]
Asmat H, Iqbal R, Sharif F, Mahmood A, Abbas A, Kashif W. Validation of bioelectrical impedance analysis for assessing dry weight of dialysis patients in Pakistan. Saudi J Kidney Dis Transpl 2017; 28(2): 285-91.
[http://dx.doi.org/10.4103/1319-2442.202766] [PMID: 28352009]
[6]
Jiang C, Patel S, Moses A, DeVita MV, Michelis MF. Use of lung ultrasonography to determine the accuracy of clinically estimated dry weight in chronic hemodialysis patients. Int Urol Nephrol 2017; 49(12): 2223-30.
[http://dx.doi.org/10.1007/s11255-017-1709-5] [PMID: 28975489]
[7]
Susantitaphong P, Laowaloet S, Tiranathanagul K, et al. Reliability of blood pressure parameters for dry weight estimation in hemodialysis patients. Ther Apher Dial 2013; 17(1): 9-15.
[http://dx.doi.org/10.1111/j.1744-9987.2012.01136.x ] [PMID: 23379487]
[8]
Liu G, Hu Y, Han Z, Jin S, Jiang Q. Genetic variant rs17185536 regulates SIM1 gene expression in human brain hypothalamus. Proc Natl Acad Sci USA 2019; 116(9): 3347-8.
[http://dx.doi.org/10.1073/pnas.1821550116] [PMID: 30755538]
[9]
Liu G, Jin S, Hu Y, Jiang Q. Disease status affects the association between rs4813620 and the expression of Alzheimer’s disease susceptibility gene TRIB3. Proc Natl Acad Sci USA 2018; 115(45): E10519-20.
[http://dx.doi.org/10.1073/pnas.1812975115] [PMID: 30355771]
[10]
Bi XA, Liu Y, Xie Y, Hu X, Jiang Q. Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment. Bioinformatics 2020; 36(8): 2561-8.
[http://dx.doi.org/10.1093/bioinformatics/btz967] [PMID: 31971559]
[11]
Chiu JS, Chong CF, Lin YF, Wu CC, Wang YF, Li YC. Applying an artificial neural network to predict total body water in hemodialysis patients. Am J Nephrol 2005; 25(5): 507-13.
[http://dx.doi.org/10.1159/000088279] [PMID: 16155360]
[12]
Xiao Y, Wu J, Lin Z, Zhao X. A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed 2018; 153: 1-9.
[http://dx.doi.org/10.1016/j.cmpb.2017.09.005] [PMID: 29157442]
[13]
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115-8.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[14]
Jiang Q, Wang G, Jin S, Li Y, Wang Y. Predicting human microRNA-disease associations based on support vector machine. Int J Data Min Bioinform 2013; 8(3): 282-93.
[http://dx.doi.org/10.1504/IJDMB.2013.056078] [PMID: 24417022]
[15]
Wang G, Wang Y, Teng M, Zhang D, Li L, Liu Y. Signal transducers and activators of transcription-1 (STAT1) regulates microRNA transcription in interferon γ-stimulated HeLa cells. PLoS One 2010; 5(7), e11794.
[http://dx.doi.org/10.1371/journal.pone.0011794] [PMID: 20668688]
[16]
Wang G, Wang Y, Feng W, et al. Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells. [J] BMC Genomics 2008; 9(Suppl. 2): S22. [J
[http://dx.doi.org/10.1186/1471-2164-9-S2-S22] [PMID: 18831788]
[17]
Zhao Y, Wang F, Juan L. MicroRNA promoter identification in arabidopsis using multiple histone markers. BioMed Res Int 2015; 2015, 861402.
[http://dx.doi.org/10.1155/2015/861402] [PMID: 26425556]
[18]
Ding Y, Tang J, Guo F. Identification of residue-residue contacts using a novel coevolution- based method. Curr Proteomics 2016; 13(2): 122-9.
[http://dx.doi.org/10.2174/157016461302160514004105]
[19]
Ding Y, Tang J, Guo F. Identification of protein-ligand binding sites by sequence information and ensemble classifier. J Chem Inf Model 2017; 57(12): 3149-61.
[http://dx.doi.org/10.1021/acs.jcim.7b00307] [PMID: 29125297]
[20]
Jiang L, Xiao Y, Ding Y, Tang J, Guo F. FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association. BMC Genomics 2018; 19(10): 911.
[http://dx.doi.org/10.1186/s12864-018-5273-x] [PMID: 30598109]
[21]
Zeng X, Liu L, Lü L, Zou Q. Prediction of potential disease-associated microRNAs using structural perturbation method. Bioinformatics 2018; 34(14): 2425-32.
[http://dx.doi.org/10.1093/bioinformatics/bty112] [PMID: 29490018]
[22]
Zhao Q, Yang Y, Ren G, Ge E, Fan C. Integrating bipartite network projection and KATZ measure to identify novel CircRNA-Disease associations. IEEE Trans Nanobioscience 2019; 18(4): 578-84.
[http://dx.doi.org/10.1109/TNB.2019.2922214] [PMID: 31199265]
[23]
Jia C, Zuo Y, Zou Q. O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinformatics 2018; 34(12): 2029-36.
[http://dx.doi.org/10.1093/bioinformatics/bty039] [PMID: 29420699]
[24]
Wei L, Luan S, Nagai LAE, Su R, Zou Q. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species. Bioinformatics 2019; 35(8): 1326-33.
[http://dx.doi.org/10.1093/bioinformatics/bty824] [PMID: 30239627]
[25]
Zou Q, Xing P, Wei L, Liu B. Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA. RNA 2019; 25(2): 205-18.
[http://dx.doi.org/10.1261/rna.069112.118] [PMID: 30425123]
[26]
Wei L, Ding Y, Su R, et al. Prediction of human protein subcellular localization using deep learning. J Parallel Distrib Comput 2018; 117: 212-7.
[http://dx.doi.org/10.1016/j.jpdc.2017.08.009]
[27]
Ding Y, Tang J, Guo F. Protein crystallization identification via fuzzy model on linear neighborhood representation. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 1-1.
[http://dx.doi.org/10.1109/TCBB.2019.2954826] [PMID: 31751248]
[28]
Wang Y, Ding Y, Tang J, Dai Y, Guo F. CrystalM: a multi-view fusion approach for protein crystallization prediction. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 1-1.
[http://dx.doi.org/10.1109/TCBB.2019.2912173] [PMID: 31027046]
[29]
Wang H, Ding Y, Tang J, et al. Identification of membrane protein types via multivariate information fusion with Hilbert-Schmidt Independence Criterion. Neurocomputing 2020; 383: 257-69.
[30]
Shen Y, Ding Y, Tang J, Zou Q, Guo F. Critical evaluation of web-based prediction tools for human protein subcellular localization. Brief Bioinform 2020; 21(5): 1628-40.
[http://dx.doi.org/10.1093/bib/bbz106] [PMID: 31697319]
[31]
Liu B, Jiang S, Zou Q. HITS-PR-HHblits: protein remote homology detection by combining PageRank and hyperlink-Induced Topic Search. Brief Bioinform 2018.
[http://dx.doi.org/10.1093/bib/bby104] [PMID: 30403770]
[32]
Qu K, Guo F, Liu X, Lin Y, Zou Q. Application of machine learning in microbiology. Front Microbiol 2019; 10: 827.
[http://dx.doi.org/10.3389/fmicb.2019.00827] [PMID: 31057526]
[33]
Ru X, Li L, Zou Q. Incorporating distance-based top-n-gram and random forest to identify electron transport proteins. J Proteome Res 2019; 18(7): 2931-9.
[http://dx.doi.org/10.1021/acs.jproteome.9b00250] [PMID: 31136183]
[34]
Ding Y, Tang J, Guo F. Identification of drug-target interactions via fuzzy bipartite local model. Neural Comput Appl 2020; 32(D1): 1-17.
[http://dx.doi.org/10.1007/s00521-019-04569-z]
[35]
Ding Y, Tang J, Guo F. Identification of drug-target interactions via multiple information integration. Inf Sci 2017; 418: 546-60.
[http://dx.doi.org/10.1016/j.ins.2017.08.045]
[36]
Ding Y, Tang J, Guo F. Identification of Drug-side effect association via semisupervised model and multiple kernel learning. IEEE J Biomed Health Inform 2019; 23(6): 2619-32.
[http://dx.doi.org/10.1109/JBHI.2018.2883834] [PMID: 30507518]
[37]
Ding Y, Tang J, Guo F. Identification of drug-side effect association via multiple information integration with centered kernel alignment. Neurocomputing 2019; 325: 211-24.
[http://dx.doi.org/10.1016/j.neucom.2018.10.028]
[38]
Ding Y, Tang J, Guo F. Identification of protein-protein interactions via a novel matrix-based sequence representation model with amino acid contact information. Int J Mol Sci 2016; 17(10): 1623.
[http://dx.doi.org/10.3390/ijms17101623] [PMID: 27669239]
[39]
Ding Y, Tang J, Guo F. Predicting protein-protein interactions via multivariate mutual information of protein sequences. BMC Bioinformatics 2016; 17(1): 398.
[http://dx.doi.org/10.1186/s12859-016-1253-9] [PMID: 27677692]
[40]
Liu H, Ren G, Chen H, et al. Predicting lncRNA-miRNA interactions based on logistic matrix factorization with neighborhood regularized. Knowl Base Syst 2020; 191, 105261.
[http://dx.doi.org/10.1016/j.knosys.2019.105261]
[41]
Passauer J, Petrov H, Schleser A, Leicht J, Pucalka K. Evaluation of clinical dry weight assessment in haemodialysis patients using bioimpedance spectroscopy: a cross-sectional study. [J] Nephrol Dial Transplant 2010; 25(2): 545-51.
[http://dx.doi.org/10.1093/ndt/gfp517] [PMID: 19808949]
[42]
Kraemer M, Rode C, Wizemann V. Detection limit of methods to assess fluid status changes in dialysis patients. Kidney Int 2006; 69(9): 1609-20.
[http://dx.doi.org/10.1038/sj.ki.5000286] [PMID: 16501488]
[43]
Jian Y, Li X, Cheng X, et al. Comparison of bioimpedance and clinical methods for dry weight prediction in maintenance hemodialysis patients. Blood Purif 2014; 37(3): 214-20.
[http://dx.doi.org/10.1159/000362109] [PMID: 24902760]
[44]
Mitchell S. Estimated Dry Weight (EDW): aiming for accuracy. Nephrol Nurs J 2002; 29(5): 421-8.
[PMID: 12434449]
[45]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[46]
Chang CC, Lin CJ. LIBSVM: A library for support vector machines . ACM 2011, Article No. 27.
[http://dx.doi.org/10.1145/1961189.1961199]
[47]
Cristianini N, Shawetaylor J, Elisseeff A, et al. On Kernel-Target Alignment. In: NIPS’01: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic. 2001; pp. 367-73.
[48]
Mariette J, Villa-Vialaneix N. Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics 2018; 34(6): 1009-15.
[http://dx.doi.org/10.1093/bioinformatics/btx682] [PMID: 29077792]
[49]
Krouwer JS, Monti KL. A simple, graphical method to evaluate laboratory assays. Eur J Clin Chem Clin Biochem 1995; 33(8): 525-7.
[PMID: 8547437]
[50]
Nalesso F, Ferrario M, Moissl U, et al. Body composition and heart rate variability to achieve dry weight and tolerance. Contrib Nephrol 2011; 171: 181-6.
[http://dx.doi.org/10.1159/000327334] [PMID: 21625109]
[51]
Bonello M, House AA, Cruz D, et al. Integration of blood volume, blood pressure, heart rate and bioimpedance monitoring for the achievement of optimal dry body weight during chronic hemodialysis. Int J Artif Organs 2007; 30(12): 1098-108.
[http://dx.doi.org/10.1177/039139880703001210] [PMID: 18203072]
[52]
Cha K, Chertow GM, Gonzalez J, Lazarus JM, Wilmore DW. Multifrequency bioelectrical impedance estimates the distribution of body water. J Appl Physiol 1995; 79(4): 1316-9.
[http://dx.doi.org/10.1152/jappl.1995.79.4.1316] [PMID: 8567578]
[53]
Ho LT, Kushner RF, Schoeller DA, et al. Bioimpedance analysis of total body water in hemodialysis patients. Kidney Int •••; 46(5): 1438-42.
[54]
Wang JH, Wang H, Wang XD, et al. Predicting drug-target interactions via FM-DNN learning. Curr Bioinform 2020; 15(1): 68-76.
[http://dx.doi.org/10.2174/1574893614666190227160538]
[55]
Wang Y, Shi FQ, Cao LY, et al. Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images. Curr Bioinform 2019; 14(4): 282-94.
[http://dx.doi.org/10.2174/1574893614666190304125221]
[56]
Fajila MNF. Gene subset selection for leukemia classification using microarray data. Curr Bioinform 2019; 14(4): 353-8.
[http://dx.doi.org/10.2174/1574893613666181031141717]

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