Machine learning-based models for screening of anemia and leukemia using features of complete blood count reports

Complete blood count (CBC) report features are routinely used to screen a wide array of hematological disorders. However, the complexity of disease overlap increases the probability of neglecting the underlying patterns between these features, and the heterogeneity associated with the subjective ass...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 33333 - 14
Hauptverfasser:	Amjad, Hafsa, Hussain, Zamir, Hasan, Mahnoor, Ul Hassan, Mahmood
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 29.09.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	631/67 631/67/2322 Adult Age groups Aged Algorithms Anemia Anemia - blood Anemia - diagnosis Artificial intelligence Blood Blood Cell Count - methods Blood diseases Blood tests CBC reports Clinical decision support Data collection Datasets Feature selection Female Females Gender Hematological diseases Hematology Hemoglobin Heterogeneity Humanities and Social Sciences Humans Laboratories Learning algorithms Leukemia Leukemia - blood Leukemia - diagnosis Leukocytes Machine Learning Male Medical personnel Medical research Middle Aged multidisciplinary Neutrophils Performance evaluation Science Science (multidisciplinary) Statistics Support Vector Machine Clinical decision support Anemia CBC reports Leukemia Machine learning
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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