Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data
| Autor: | Anton Gradišek, Erik Dovgan, Hee Jung Chung, Hyung Woo Kim, Mina Hur, Shabbir Syed-Abdul, Mohy Uddin, Rianda Putra Firdani, Jaehyeon Park |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Support Vector Machine Adolescent lcsh:Medicine Information technology Machine learning computer.software_genre Logistic regression Article Cross-validation Diagnosis Differential Machine Learning Cancer screening Young Adult Artificial Intelligence Biomarkers Tumor Humans Medicine Cell Population Data Author Correction lcsh:Science Aged Haematological cancer Multidisciplinary Data collection Artificial neural network business.industry lcsh:R Linear model Middle Aged Prognosis Support vector machine Hematologic Neoplasms Data analysis Female lcsh:Q Neural Networks Computer Artificial intelligence business computer Algorithms Haematological diseases Follow-Up Studies |
| Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-8 (2020) Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-020-61247-0 |
| Popis: | 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 non-malignancy) 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. |
| Databáze: | OpenAIRE |
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