Article (Scientific journals)
An efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis.
Martins Conde, Patricia; Sauter, Thomas; Nguyen, Thanh-Phuong
2020In Scientific Reports, 10 (1), p. 20613
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Abstract :
[en] Hereditary haemochromatosis (HH) is an autosomal recessive disease, where HFE C282Y homozygosity accounts for 80-85% of clinical cases among the Caucasian population. HH is characterised by the accumulation of iron, which, if untreated, can lead to the development of liver cirrhosis and liver cancer. Since iron overload is preventable and treatable if diagnosed early, high-risk individuals can be identified through effective screening employing artificial intelligence-based approaches. However, such tools expose novel challenges associated with the handling and integration of large heterogeneous datasets. We have developed an efficient computational model to screen individuals for HH using the family study data of the Hemochromatosis and Iron Overload Screening (HEIRS) cohort. This dataset, consisting of 254 cases and 701 controls, contains variables extracted from questionnaires and laboratory blood tests. The final model was trained on an extreme gradient boosting classifier using the most relevant risk factors: HFE C282Y homozygosity, age, mean corpuscular volume, iron level, serum ferritin level, transferrin saturation, and unsaturated iron-binding capacity. Hyperparameter optimisation was carried out with multiple runs, resulting in 0.94 ± 0.02 area under the receiving operating characteristic curve (AUCROC) for tenfold stratified cross-validation, demonstrating its outperformance when compared to the iron overload screening (IRON) tool.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Martins Conde, Patricia ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit
Sauter, Thomas ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)
Nguyen, Thanh-Phuong ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit
External co-authors :
no
Language :
English
Title :
An efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis.
Publication date :
2020
Journal title :
Scientific Reports
ISSN :
2045-2322
Publisher :
Nature Publishing Group, London, United Kingdom
Volume :
10
Issue :
1
Pages :
20613
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Systems Biomedicine
Available on ORBilu :
since 31 December 2020

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