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Hemoglobin
international journal for hemoglobin research
Volume 46, 2022 - Issue 6
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Research Articles

Prediction of Heart and Liver Iron Overload in β-Thalassemia Major Patients Using Machine Learning Methods

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Pages 303-307 | Received 23 Sep 2022, Accepted 11 Oct 2022, Published online: 07 Feb 2023

References

  • Longo F, Piolatto A, Ferrero GB, Piga A. Ineffective erythropoiesis in β-thlassaemia: key steps and therapeutic options by drugs. IJMS. 2021;22(13):7229.
  • Najafipour F, Aliasgarzadeh A, Aghamohamadzadeh N, et al. A cross-sectional study of metabolic and endocrine complications in beta-thalassemia major. Ann Saudi Med. 2008;28(5):361–366.
  • Majd Z, Haghpanah S, Ajami GH, et al. Serum ferritin levels correlation with heart and liver MRI and LIC in patients with transfusion-dependent thalassemia. Iran Red Crescent Med J. 2015;17(4):e24959.
  • Wahidiyat PA, Liauw F, Sekarsari D, Putriasih SA. Evaluation of cardiac and hepatic iron overload in thalassemia major patients with T2* magnetic resonance imaging. Hematology. 2017;22(8):501–507.
  • Cunningham MJ, Macklin EA, Neufeld EJ, et al.; Thalassemia Clinical Research Network. Complications of beta-thalassemia major in North America. Blood. 2004;104(1):34–39.
  • Azarkeivan A, Hashemieh M, Shirkavand A, et al. Correlation between heart, liver and pancreas hemosiderosis measured by MRI T2* among thalassemia major patients from Iran. Arch Iran Med. 2016;19(2):96–100.
  • Krittanawong C, Virk HUH, Kumar A, et al. Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection. Sci Rep. 2021;11(1):8992.
  • Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019;19(1):281.
  • Breiman L. Random Forests. In: Schapire RE, editor, Machine learning. Dordrecht: Kluwer Academic Publishers; 2001.
  • Couronné R, Probst P, Boulesteix AL. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics. 2018;19(1):270.
  • Klug M, Barash Y, Bechler S, et al. A gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a nine-point triage score. J Gen Intern Med. 2020;35(1):220–227.
  • Sarker IH. Machine Learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160.
  • Nusinovici S, Tham YC, Chak Yan MY, et al. Logistic regression was as good as machine learning for predicting major chronic diseases. J Clin Epidemiol. 2020;122:56–69.
  • Alonso-Betanzos A, Bolón-Canedo V. Big-data analysis, cluster analysis, and machine-learning approaches. Adv Exp Med Biol. 2018;1065:607–626.
  • Mooney SJ, Pejaver V. Big data in public health: terminology, machine learning, and privacy. Annu Rev Public Health. 2018;39:95–112.
  • Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262–e273.
  • Nembrini S, König IR, Wright MN. The revival of the Gini importance? Bioinformatics. 2018;34(21):3711–3718.
  • Bartholomai JA, Frieboes HB. Lung cancer survival prediction via machine learning regression, classification, and statistical techniques. [Abstract #183718001]. Proceedings of the Institute for Electrical and Electronics Engineers (IEEE), International Symposium on Signal Processing and Information Technology (ISSPIT), held at Louisville, KY, USA, on December 6–8, 2018. Proc Int Symp Signal Proc Inf Tech. Inf Tech. 2018;2018:632–637.
  • Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18(1):462.
  • Feeny AK, Rickard J, Patel D, et al. Machine learning prediction of response to cardiac resynchronization therapy: improvement versus current guidelines. Circ Arrhythm Electrophysiol. 2019;12(7):e007316.
  • Buoso S, Joyce T, Kozerke S. Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks. Med Image Anal. 2021;71:102066.
  • Yang Z, Xu Q, Bao S, et al. Learning with Multiclass AUC: theory and algorithms. IEEE Trans Pattern Anal Mach Intell. 2022;44(11):7747–7763.
  • Alberg AJ, Park JW, Hager BW, et al. The use of “overall accuracy” to evaluate the validity of screening or diagnostic tests. J Gen Intern Med. 2004;19(5 Pt 1):460–465.
  • Trevethan R. Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health. 2017;5:307.
  • Sevimli C, Yilmaz Y, Bayramoglu Z, et al. Pancreatic MR imaging and endocrine complications in patients with beta-thalassemia: a single-center experience. Clin Exp Med. 2022;22(1):95–101.
  • Rostoker G, Griuncelli M, Loridon C, et al. Reassessment of iron biomarkers for prediction of dialysis iron overload: an MRI study. PloS One. 2015;10(7):e0132006.
  • Chirico V, Rigoli L, Lacquaniti A, et al. Endocrinopathies, metabolic disorders, and iron overload in major and intermedia thalassemia: serum ferritin as diagnostic and predictive marker associated with liver and cardiac T2* MRI assessment. Eur J Haematol. 2015;94(5):404–412.
  • Chen X, Zhang H, Yang Q, et al. Value of severe liver iron overload for assessing heart iron levels in thalassemia major patients. J Magn Reson Imaging. 2016;44(4):880–889.
  • Derchi G, Dessì C, Bina P, Webthal®, et al. Risk factors for heart disease in transfusion-dependent thalassemia: serum ferritin revisited. Intern Emerg Med. 2019;14(3):365–370.
  • Silvilairat S, Charoenkwan P, Saekho S, et al. Heart rate variability for early eetection of cardiac iron deposition in patients with transfusion-dependent thalassemia. PloS One. 2016;11(10):e0164300.
  • Ojha V, Ganga KP, Seth T, et al. Role of CMR feature-tracking derived left ventricular strain in predicting myocardial iron overload and assessing myocardial contractile dysfunction in patients with thalassemia major. Eur Radiol. 2021;31(8):6184–6192.
  • Bayraktaroglu S, Karadas N, Onen S, et al. Modern management of iron overload in thalassemia major patients guided by MRI techniques: real-world data from a long-term cohort study. Ann Hematol. 2022;101(3):521–529.
  • Aggarwal P, Kumar I, Jain A, et al. Relation between cardiac T2* values and electrocardiographic parameters in children with transfusion-dependent thalassemia. J Pediatr Hematol Oncol. 2020;42(7):e610–e614.
  • Wahidiyat PA, Iskandar SD, Sekarsari D. Evaluation of iron overload between age groups using magnetic resonance imaging and its correlation with iron profile in transfusion-dependent thalassemia. Acta Med Indones. 2018;50(3):230–236.

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