Machine learning approach in mortality rate prediction for hemodialysis patients

References

• Bakkal H, Dizdar OS, Erdem S, Kulakoğlu S, Akcakaya B, Katırcılar Y, Uludag K. 2020. The relationship between hand grip strength and nutritional status determined by malnutrition inflammation score and biochemical parameters in hemodialysis patients. J Renal Nutr. 30(6):548–555.
   PubMed Web of Science ®Google Scholar
• Bulut H, Onan A, Korukoğlu S. 2020. An improved ant-based algorithm based on heaps merging and fuzzy c-means for clustering cancer gene expression data. Sādhanā. 45(160):1–17.
   Google Scholar
• Cozzolino M, Mangano M, Stucchi A, Ciceri P, Conte F, Galassi A. 2018. Cardiovascular disease in dialysis patients. Nephrol Dial Transplant. 33(6):28–34.
   Google Scholar
• Duan ZY, Jijun L, Cai G, Xiangmei C, Fengkun C. 2019. Prevalence, outcome and modalities of renal replacement therapy in burn patients: a systematic review and meta-analysis. Nephrol Dial Transplant. 34(5):gfz106.FP710.
   Google Scholar
• Escudero J, Ifeachor E, Zajicek JP, Green C, Shearer J, Pearson S. 2013. Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease. IEEE Trans Biomed Eng. 60(1):164–168.
   PubMed Web of Science ®Google Scholar
• Frid A, Shor M, Shifrin A, Yarnitsky D, Granovsky Y. 2020. A biomarker for discriminating between Migraine with and without aura: machine learning on functional connectivity on resting-state EEGs. Ann Biomed Eng. 48(1):403–412.
   PubMed Web of Science ®Google Scholar
• Glowacz A, Glowacz Z. 2016. Recognition of images of finger skin with application of histogram, image filtration and K-NN classifier. Biocybernet Biomed Eng. 36(1):95–101.
   Web of Science ®Google Scholar
• Han J, Kamber M. 2007. Data mining concepts and techniques. 2nd ed. Massachusetts, USA: Morgan Kaufmann Publishers.
   Google Scholar
• Jagieła J, Bartnicki P, Rysz J. 2020. Selected cardiovascular risk factors in early stages of chronic kidney disease. Int Urol Nephrol. 52(2):303–314.
   PubMed Web of Science ®Google Scholar
• Levin A, Stevens PE, Bilous RW, Coresh J, De Francisco ALM, Francisco D, De Jong PE, Griffith KE, Hemmelgarn BR, Iseki K, et al. 2013. Kidney disease: improving global outcomes (KDIGO) CKD work group (2013). KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 3(1):1–150.
   Web of Science ®Google Scholar
• Li Q, Rajagopalan C, Clifford GD. 2014. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng. 61(6):1607–1613.
   PubMed Web of Science ®Google Scholar
• Liakopoulos V, Roumeliotis S, Zarogiannis S, Eleftheriadis T, Mertens PR. 2019. Oxidative stress in hemodialysis: causative mechanisms, clinical implications, and possible therapeutic interventions. Semin Dial. 32(1):58–71. Berkeley, USA: Statistical Laboratory of the University of California.
   PubMed Web of Science ®Google Scholar
• MacQueen JE. 1967. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkley Symposium on Mathematical Statistics and Probability, vol. 1; p. 281–297.
   Google Scholar
• Mitchell C, Rahko PS, Blauwet LA, Canaday B, Finstuen JA, Foster MC, Horton K, Ogunyankin KO, Palma RA, Velazquez EJ. 2019. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of echocardiography. J Am Soc Echocardiogr. 32(1):1–64.
   PubMed Web of Science ®Google Scholar
• Nuhu F, Bhandari S. 2018. Oxidative stress and cardiovascular complications in chronic kidney disease, the impact of anaemia. Pharmaceuticals. 11(4):103.
   Web of Science ®Google Scholar
• Nuryani N, Ling SSH, Nguyen HT. 2012. Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection. Ann Biomed Eng. 40(4):934–945.
   PubMed Web of Science ®Google Scholar
• Obrador GT, Rubilar X, Agazzi E, Estefan J. 2016. The challenge of providing renal replacement therapy in developing countries: the Latin American perspective. Am J Kidney Dis. 67(3):499–506. 2015.08.033.
   PubMed Web of Science ®Google Scholar
• Onan A. 2015. A fuzzy-rough nearest neighbour classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Expert Syst Appl. 42(20):6844–6852.
   Web of Science ®Google Scholar
• Onan A. 2016. Classifier and feature set ensembles for web page classification. J Inform Sci. 42(2):150–165.
   Google Scholar
• Onan A. 2018. Biomedical text categorization based on ensemble pruning and optimized topic modelling. Comput Math Methods Med. 2018:2497471.
   PubMed Web of Science ®Google Scholar
• Onan A. 2019. Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering. IEEE Access. 7:145614–145633.
   Web of Science ®Google Scholar
• Onan A. 2020. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurrency Comput Practice Exp. https://doi.org/https://doi.org/10.1002/cpe.5909
   Google Scholar
• Onan A, Korukoğlu S, Bulut H. 2016. Ensemble of keyword extraction methods and classifiers in text classification. Expert Syst Appl. 57:232–247.
   Web of Science ®Google Scholar
• Pham TT, Thamrin C, Robinson PD, McEwan AL, Leong PHW. 2017. Respiratory artefact removal in forced oscillation measurements: a machine learning approach. IEEE Trans Biomed Eng. 64(8):1679–1687.
   PubMed Web of Science ®Google Scholar
• Rovini E, Maremmani C, Moschetti A, Esposito D, Cavallo F. 2018. Comparative motor pre-clinical assessment in Parkinson's disease using supervised machine learning approaches. Ann Biomed Eng. 46(12):2057–2068.
   PubMed Web of Science ®Google Scholar
• Russel S, Norvig P. 2016. Artificial intelligence: a modern approach. 3rd ed. United Kingdom: Pearson.
   Google Scholar
• Sapp JL, Bar-Tal M, Howes AJ, Toma JE, El-Damaty A, Warren JW, MacInnis PJ, Zhou S, Horáček BM. 2017. Real-time localization of ventricular tachycardia origin from the 12-Lead electrocardiogram. JACC Clin Electrophysiol. 3(7):687–699.
   PubMedGoogle Scholar
• Shaikhina T, Khovanova N. 2017. Handling limited datasets with neural networks in medical applications: a small-data approach. Artif Intell Med. 75:51–63.
   PubMed Web of Science ®Google Scholar
• Shaikhina T, Lowe D, Daga S, Briggs D, Higgins R, Khovanova N. 2019. Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control. 52:456–462.
   Web of Science ®Google Scholar
• Shaikhina T, Lowe D, Daga S, Briggs D, Higgins R, Khovanova N. 2015. Machine learning for predictive modeling based on small data in biomedical engineering. IFAC Pap Online. 48(20):469–474.
   Google Scholar
• Tao R, Zhang S, Huang X, Tao M, Ma J, Ma S, Zhang C, Zhang T, Tang F, Lu J, et al. 2019. Magnetocardiography based ischemic heart disease detection and localization using machine learning methods. IEEE Trans Biomed Eng. 66(6):1658–1667.
   PubMed Web of Science ®Google Scholar
• Vapnik V. 1995. The nature of statistical learning theory. Berlin: Springer-Verlag.
   Google Scholar
• Zhang Y, Murugesan P, Huang K, Cai H. 2020. NADPH oxidases and oxidase crosstalk in cardiovascular diseases: novel therapeutic targets. Nat Rev Cardiol. 17(3):170–194.
   PubMed Web of Science ®Google Scholar
• Zhou S, Abdelwahab A, Sapp JL, Warren JW, Horacek BM. 2019. Localization of ventricular activation origin from the 12-Lead ECG: a comparison of linear regression with non-linear methods of machine learning. Ann Biomed Eng. 47(2):403–412.
   PubMed Web of Science ®Google Scholar