An efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis.
Pubmed ID: 33244054
Pubmed Central ID: PMC7691515
Journal: Scientific reports
Publication Date: 11/26/2020
Affiliation: University of Luxembourg, Esch-sur-Alzette, Luxembourg. firstname.lastname@example.org.
MeSH Terms: Humans, Male, Adult, Female, Cohort Studies, Homozygote, Artificial Intelligence, Middle Aged, Young Adult, Mass Screening, Iron, Hemochromatosis, Iron Overload, Hemochromatosis Protein, Machine Learning
Authors: Martins Conde P, Sauter T, Nguyen TP
Cite As: Martins Conde P, Sauter T, Nguyen TP. An efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis. Sci Rep 2020 Nov 26;10(1):20613.
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.