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- W4313492793 abstract "<sec> <title>BACKGROUND</title> Childhood leukemia is one of the most prevalent forms of pediatric cancer and occurs in two forms; Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). Prompt treatment of these ailments has been shown to significantly improve the survival rate of children afflicted with acute leukemia. </sec> <sec> <title>OBJECTIVE</title> In an effort to develop an early and comprehensive predictor of hematologic malignancy in children. </sec> <sec> <title>METHODS</title> we studied nutritional markers, key indicators of leukemia, and granulocytes in the patient's blood. Applying a machine learning algorithm and ten indices, our team analyzed 826 pediatric patients with ALL and 255 children with AML, comparing them with a control group of 200 healthy children. </sec> <sec> <title>RESULTS</title> The study uncovered noteworthy distinctions between boys and girls, as well as the relationship between blood biochemical markers. Through the use of a random forest model, we achieved an Area Under the Curve (AUC) of 0.950 for the prediction of leukemia subtypes, and an AUC of 0.909 for the prediction of AML. </sec> <sec> <title>CONCLUSIONS</title> This research provides an efficient and auxiliary diagnostic tool for the early screening of childhood blood cancers, and demonstrates the potential of artificial intelligence in modern healthcare. </sec>" @default.
- W4313492793 created "2023-01-06" @default.
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- W4313492793 date "2022-12-30" @default.
- W4313492793 modified "2023-09-24" @default.
- W4313492793 title "Unveiling the Prognostic Potential of Predictive Indices in Childhood Acute Leukemia using Artificial Intelligence Techniques: Results from 10 years of Hospital Information System Data (Preprint)" @default.
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- W4313492793 doi "https://doi.org/10.2196/preprints.45416" @default.
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