Volume 21, Issue 2 (Summer 2024)
Sci J Iran Blood Transfus Organ 2024, 21(2): 151-159 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Esmaielpour A, Ameli M, Mozdgir A, Ahmadi O, Zarabi M. Predicting pre-collection umbilical cord blood clotting using advanced machine learning algorithms. Sci J Iran Blood Transfus Organ 2024; 21 (2) :151-159
URL: http://bloodjournal.ir/article-1-1520-en.html
URL: http://bloodjournal.ir/article-1-1520-en.html
Full-Text [PDF 535 kb]
(227 Downloads)
| Abstract (HTML) (931 Views)
Full-Text: (264 Views)
References:
Predicting pre-collection umbilical cord blood clotting
using advanced machine learning algorithms
Esmaielpour A.H.1, Ameli M.1, Mozdgir A.1, Ahmadi O.1, Zarabi M.2
1Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
2Department of Regenerative Medicine, Royan Institute for Stem Cell Biology and Technology, Tehran, Iran
Abstract
Background and Objectives
Umbilical cord blood is a valuable source of stem cells used in transplants to treat various diseases including leukemia, lymphoma and genetic disorders. However, cord blood clotting during the collection process can reduce sample quality and quantity and impact its efficacy in cord blood banking. This article aims to predict pre-collection cord blood clotting in donors using advanced machine learning techniques.
Materials and Methods
In this retrospective study, data was gathered using 928127 samples available in the fetal cord blood bank, and with using supervised machine learning classification algorithms, including decision tree, naïve Bayes, K-Nearest Neighbors, Support vector machine, Random forest, Majority voting and Multilayer perceptron, prediction of cord blood clotting was performed on the Royan cord blood bank database and their performance was compared using evaluation metrics such as Accuracy, Precision, Recall, and F1 Score.
Results
In this study, the algorithm accuracy of Decision Tree was 0.80, Naive Bayes was 0.63, K-Nearest Neighbors was 0.83, Support Vector Machine was 0.65, Random Forest was 0.84, Majority Voting Classifier was 0.81, and Multilayer Perceptron was 0.74.
Conclusions
In this study, the performance of Random Forest and K-Nearest Neighbors algorithms demonstrated the best accuracy showing that machine learning algorithms can predict prenatal cord blood clotting with high accuracy which can help prevent sampling of clotted specimens in order to reduce costs and storage problems.
Key words: Stem Cells, Machine Learning, Umbilical Cord Blood, Bioanformatics
Received: 8 Jan 2024
Accepted: 10 Jun 2024
Correspondence: Ameli M., PhD in Industrial Engineering. Assistant Professor of Department of Industrial Engineering, Faculty of Engineering, Kharazmi University.
Postal Code: 1571914911, Tehran, Iran. Tel: (+9826) 34523124; Fax: (+9821) 88825580
E-mail: m.ameli @khu.ac.ir
- Pezeshki SMS, Ghasemzadeh M, Hosseini E. Cord blood stem cells: an overview of biology and current applications. Sci J Iran Blood Transfus Organ 2023; 20(4): 335-45. [Article in Farsi]
- Niazi V, heydari Keshel S, Shahbazi M. Advances and challenges in storage, transplantation, expansion and homing of Umbilical Cord Blood Hematopoietic Stem Cells (UCB-HSCs). Sci J Iran Blood Transfus Organ 2020; 17(3): 226-41. [Article in Farsi]
- Zhu D, Barabadi M, McDonald C, Kusuma G, Inocencio IM, Lim R. Implications of maternal-fetal health on perinatal stem cell banking. Gene Ther 2024; 31(3): 65-73.
- Fritz MA, Christopher CR. Umbilical vein thrombosis and maternal diabetes mellitus. J Reprod Med 1981; 26(6): 320-4.
- Heifetz SA. Thrombosis of the Umbilical Cord: Analysis of 52 Cases and Literature Review. Pediatr Pathol 1988; 8(1): 37-54.
- Schröcksnadel H, Holböck E, Mitterschiffthaler G, Tötsch M, Dapunt O. Thrombotic occlusion of an umbilical vein varix causing fetal death. Archives of Gynecology and Obstetrics. 1991;248(4):213-5.
- Lox CD, Word RA, Jeter M, Corrigan JJ. Cord blood coagulation changes in maternal hypertensign. Pediatr Res 1984; 18(4): 317.
- Funk A, Buechel J, Huhn EA, Mueller D, Granado C, Tsakiris D, et al. Antenatal predictors of stem cell content for successful umbilical cord blood donation. Arch Gynecol Obstet 2021; 304(2): 377-84.
- Haghbayan MH, Karimi B, Mozdgir A, Abbaspanah B. Increasing the Efficacy of Umbilical Cord Blood Banking Using Machine Learning Algorithms: A Case Study from Royan Cord Blood Bank. Scientia Iranica 2023; 1-26. [Article in press]
- Hare J, DeLeon PG, Pool K, Reioux D, Fontenot M, Champlin RE, et al. Optimal umbilical cord blood collection, processing and cryopreservation methods for sustained public cord blood banking. Cytotherapy 2021; 23(11): 1029-35.
- Jamshidi R, Rajabpour Sanati S, Zarrabi M. A New Method to Predict the Quality of Umbilical Cord Blood Units based on Maternal and Neonatal Factors and Collection Techniques. Journal of Applied Research on Industrial Engineering 2023; 10(2): 218-37.
- Raschka S, Patterson J, Nolet C. Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Information 2020; 11(4): 93.
- Barbieri MC, Grisci BI, Dorn M. Analysis and comparison of feature selection methods towards performance and stability. Expert Systems with Applications 2024; 249: 123667.
- Kaur H, Pannu HS, Malhi AK. A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions. ACM Comput Surv 2019; 52(4): 1-36.
- Alfian G, Syafrudin M, Fahrurrozi I, Fitriyani NL, Atmaji FT, Widodo T, et al. Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method. Computers 2022; 11(9): 136.
- Mezzatesta S, Torino C, Meo PD, Fiumara G, Vilasi A. A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Comput Methods Programs Biomed 2019; 177: 9-15.
- Feuerriegel S, Frauen D, Melnychuk V, Schweisthal J, Hess K, Curth A, et al. Causal machine learning for predicting treatment outcomes. Nat Med 2024; 30(4): 958-68.
- Asnicar F, Thomas AM, Passerini A, Waldron L, Segata N. Machine learning for microbiologists. Nat Rev Microbiol 2024; 22(4): 191-205.
|
Sci J Iran Blood Transfus Organ 2024; 21 (2): 151-159 |
|
Original Article
|
using advanced machine learning algorithms
Esmaielpour A.H.1, Ameli M.1, Mozdgir A.1, Ahmadi O.1, Zarabi M.2
1Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
2Department of Regenerative Medicine, Royan Institute for Stem Cell Biology and Technology, Tehran, Iran
Abstract
Background and Objectives
Umbilical cord blood is a valuable source of stem cells used in transplants to treat various diseases including leukemia, lymphoma and genetic disorders. However, cord blood clotting during the collection process can reduce sample quality and quantity and impact its efficacy in cord blood banking. This article aims to predict pre-collection cord blood clotting in donors using advanced machine learning techniques.
Materials and Methods
In this retrospective study, data was gathered using 928127 samples available in the fetal cord blood bank, and with using supervised machine learning classification algorithms, including decision tree, naïve Bayes, K-Nearest Neighbors, Support vector machine, Random forest, Majority voting and Multilayer perceptron, prediction of cord blood clotting was performed on the Royan cord blood bank database and their performance was compared using evaluation metrics such as Accuracy, Precision, Recall, and F1 Score.
Results
In this study, the algorithm accuracy of Decision Tree was 0.80, Naive Bayes was 0.63, K-Nearest Neighbors was 0.83, Support Vector Machine was 0.65, Random Forest was 0.84, Majority Voting Classifier was 0.81, and Multilayer Perceptron was 0.74.
Conclusions
In this study, the performance of Random Forest and K-Nearest Neighbors algorithms demonstrated the best accuracy showing that machine learning algorithms can predict prenatal cord blood clotting with high accuracy which can help prevent sampling of clotted specimens in order to reduce costs and storage problems.
Key words: Stem Cells, Machine Learning, Umbilical Cord Blood, Bioanformatics
Received: 8 Jan 2024
Accepted: 10 Jun 2024
Correspondence: Ameli M., PhD in Industrial Engineering. Assistant Professor of Department of Industrial Engineering, Faculty of Engineering, Kharazmi University.
Postal Code: 1571914911, Tehran, Iran. Tel: (+9826) 34523124; Fax: (+9821) 88825580
E-mail: m.ameli @khu.ac.ir
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
