A Machine Learning Model for Predicting Fetal Hemoglobin Levels in Sickle Cell Disease Patients

Konstantinos Oikonomou, Kathleen Steinhöfel, Stephan Menzel

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Abstract

Sickle cell disease is one of the commonest genetic diseases and is defined as a decrease in hemoglobin concentration in the blood. The main known factor that can alleviate the disease is the persistence of fetal hemoglobin (HbF), and thus the aim of our research is to build a model to predict the HbF% of patients based on the three regulating genes of the disease (BCL11A, Xmm1-HBG2 and HBS1L-MYB). A machine learning approach is employed in order to improve the accuracy of the model, with various algorithms of that type being explored. In the end, the K-nearest neighbors algorithm is chosen and an initial version of it is implemented and tested. Finally, the algorithm is optimized enabling our optimized model to predict the HbF% of a patient with 87.25% accuracy, a major improvement over the existing alternative that has a mean error of 336.33%. Furthermore, 93.45% of our predictions have a sheer error that is less than 0.5, and all these facts reinforce the strength of our model as a quick and accurate estimation tool for small and medium-sized clinical trials, where fast HbF% predictions can help adjust for genetic background variability that obscures test outcomes.

Original languageEnglish
Title of host publicationProceedings of Sixth International Congress on Information and Communication Technology
EditorsXin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages79-91
Number of pages13
Volume1
ISBN (Print)9789811623769
DOIs
Publication statusE-pub ahead of print - 24 Sept 2021
Event6th International Congress on Information and Communication Technology, ICICT 2021 - Virtual, Online
Duration: 25 Feb 202126 Feb 2021

Publication series

NameLecture Notes in Networks and Systems
Volume235
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference6th International Congress on Information and Communication Technology, ICICT 2021
CityVirtual, Online
Period25/02/202126/02/2021

Keywords

  • Fetal hemoglobin
  • Machine learning prediction model
  • Sickle cell disease

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