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Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.

Scientific reports - 6 Jul 2020

Behravan Hamid, Hartikainen Jaana M, Tengström Maria, Kosma Veli-Matti, Mannermaa Arto

Abstract excerpt

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of...

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Identifiers

PMID: 32632202
DOI: 10.1038/s41598-020-66907-9
PMCID: PMC7338351

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