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

Shabbir Syed-Abdul¹, Rianda-Putra Firdani¹, Hee-Jung Chung^2,3

¹Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.

Scientific Reports

|March 18, 2020

Summary

This summary is machine-generated.

Machine learning (ML) models can screen hematologic malignancies using cell population data (CPD). The artificial neural network (ANN) model demonstrated superior diagnostic performance, aiding clinical laboratory screening.

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Area of Science:

Hematology
Medical Diagnostics
Machine Learning

Background:

Cell Population Data (CPD) offers valuable blood cell parameters for differential diagnosis.
Machine Learning (ML) is transforming medical diagnostics through advanced data analytics.

Purpose of the Study:

To develop and evaluate ML algorithms for screening hematologic malignancies using CPD.
To compare the performance of seven ML models in identifying hematologic cancers.

Main Methods:

Utilized a dataset of 882 cases (457 hematologic malignancy, 425 non-malignancy) from Konkuk University Medical Center.
Applied seven ML models: SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN.
Employed stratified 10-fold cross-validation and evaluated performance using accuracy, precision, recall, and AUC.

Main Results:

The Artificial Neural Network (ANN) model exhibited the highest performance among all tested ML models.
ANN achieved an accuracy of 82.8%, precision of 82.8%, recall of 84.9%, and AUC of 93.5% ± 2.6%.

Conclusions:

ANN models utilizing CPD show significant potential as an efficient tool for clinical laboratory screening of hematologic malignancies.
The findings support further research into applying ML for broader clinical practice in hematology.