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Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Authors
 Shabbir Syed-Abdul  ;  Rianda-Putra Firdani  ;  Hee-Jung Chung  ;  Mohy Uddin  ;  Mina Hur  ;  Jae Hyeon Park  ;  Hyung Woo Kim  ;  Anton Gradiš다  ;  Erik Dovgan 
Citation
 Scientific Reports, Vol.10 : 4583, 2020 
Journal Title
 Scientific Reports 
Issue Date
2020
Abstract
Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic nonmalignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.
Files in This Item:
T999920201.pdf Download
DOI
10.1038/s41598-020-61247-0
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hyung Woo(김형우) ORCID logo https://orcid.org/0000-0002-6305-452X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/175401
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