An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia

Yousra El Alaoui; Regina Padmanabhan; Adel Elomri; Marwa K. Qaraqe; Halima El Omri; Ruba Yasin Taha

doi:10.3233/SHTI230479

An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia

Yousra El Alaoui^*, Regina Padmanabhan, Adel Elomri, Marwa K. Qaraqe, Halima El Omri, Ruba Yasin Taha

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms. A comparison between the performances of the three models demonstrates that Decision Tree classifier outperformed XGBoost and Random Forest algorithms in ALL detection using CBC-based records.

Original language	English
Title of host publication	Healthcare Transformation with Informatics and Artificial Intelligence
Editors	John Mantas, Parisis Gallos, Emmanouil Zoulias, Arie Hasman, Mowafa S. Househ, Martha Charalampidou, Andriana Magdalinou
Publisher	IOS Press BV
Pages	265-268
Number of pages	4
ISBN (Electronic)	9781643684000
DOIs	https://doi.org/10.3233/SHTI230479
Publication status	Published - 29 Jun 2023
Event	21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023 - Athens, Greece Duration: 1 Jul 2023 → 3 Jul 2023

Publication series

Name	Studies in Health Technology and Informatics
Volume	305
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023
Country/Territory	Greece
City	Athens
Period	1/07/23 → 3/07/23

Keywords

ALL
CBC
Early detection
Machine learning

Access to Document

10.3233/SHTI230479

Cite this

El Alaoui, Y., Padmanabhan, R., Elomri, A., Qaraqe, M. K., El Omri, H., & Yasin Taha, R. (2023). An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia. In J. Mantas, P. Gallos, E. Zoulias, A. Hasman, M. S. Househ, M. Charalampidou, & A. Magdalinou (Eds.), Healthcare Transformation with Informatics and Artificial Intelligence (pp. 265-268). (Studies in Health Technology and Informatics; Vol. 305). IOS Press BV. https://doi.org/10.3233/SHTI230479

El Alaoui, Yousra ; Padmanabhan, Regina ; Elomri, Adel et al. / An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia. Healthcare Transformation with Informatics and Artificial Intelligence. editor / John Mantas ; Parisis Gallos ; Emmanouil Zoulias ; Arie Hasman ; Mowafa S. Househ ; Martha Charalampidou ; Andriana Magdalinou. IOS Press BV, 2023. pp. 265-268 (Studies in Health Technology and Informatics).

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title = "An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia",

abstract = "This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms. A comparison between the performances of the three models demonstrates that Decision Tree classifier outperformed XGBoost and Random Forest algorithms in ALL detection using CBC-based records.",

keywords = "ALL, CBC, Early detection, Machine learning",

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note = "Publisher Copyright: {\textcopyright} 2023 The authors and IOS Press.; 21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023 ; Conference date: 01-07-2023 Through 03-07-2023",

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El Alaoui, Y, Padmanabhan, R, Elomri, A , Qaraqe, MK, El Omri, H & Yasin Taha, R 2023, An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia. in J Mantas, P Gallos, E Zoulias, A Hasman, MS Househ, M Charalampidou & A Magdalinou (eds), Healthcare Transformation with Informatics and Artificial Intelligence. Studies in Health Technology and Informatics, vol. 305, IOS Press BV, pp. 265-268, 21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023, Athens, Greece, 1/07/23. https://doi.org/10.3233/SHTI230479

An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia. / El Alaoui, Yousra; Padmanabhan, Regina; Elomri, Adel et al.
Healthcare Transformation with Informatics and Artificial Intelligence. ed. / John Mantas; Parisis Gallos; Emmanouil Zoulias; Arie Hasman; Mowafa S. Househ; Martha Charalampidou; Andriana Magdalinou. IOS Press BV, 2023. p. 265-268 (Studies in Health Technology and Informatics; Vol. 305).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Padmanabhan, Regina

AU - Elomri, Adel

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AU - El Omri, Halima

AU - Yasin Taha, Ruba

PY - 2023/6/29

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AB - This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms. A comparison between the performances of the three models demonstrates that Decision Tree classifier outperformed XGBoost and Random Forest algorithms in ALL detection using CBC-based records.

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BT - Healthcare Transformation with Informatics and Artificial Intelligence

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A2 - Zoulias, Emmanouil

A2 - Hasman, Arie

A2 - Househ, Mowafa S.

A2 - Charalampidou, Martha

A2 - Magdalinou, Andriana

PB - IOS Press BV

T2 - 21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

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El Alaoui Y, Padmanabhan R, Elomri A , Qaraqe MK, El Omri H, Yasin Taha R. An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia. In Mantas J, Gallos P, Zoulias E, Hasman A, Househ MS, Charalampidou M, Magdalinou A, editors, Healthcare Transformation with Informatics and Artificial Intelligence. IOS Press BV. 2023. p. 265-268. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI230479

An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia

Abstract

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Conference

Keywords

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EX-QNRF-NPRPS-4: An Operations Management Approach for the Improvement of Cancer Care Delivery in Qatar: Predictive Models, Analysis, and Pathways Optimization for Patients with Hematological Malignancies.

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An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

EX-QNRF-NPRPS-4: An Operations Management Approach for the Improvement of Cancer Care Delivery in Qatar: Predictive Models, Analysis, and Pathways Optimization for Patients with Hematological Malignancies.

Cite this