Menu
Outline of the proposed breast cancer risk prediction system using ML. In the training phase, networks of interacting genetic and demographic risk factors for BC are identified. These networks of features are then used to predict whether an unlabelled individual is a cancer case or a healthy control in the testing phase. This study provides two examples showing that a combination of interacting genetic variants (SNPs) with BC risk factors related to both familial history and oestrogen metabolism can increase BC risk prediction accuracy.
Predicting the risk of breast cancer is a complex task that involves considering a variety of interacting genetic and demographic factors. Machine learning algorithms can be used to analyze these factors and make predictions about an individual’s risk of developing breast cancer.
One approach to predicting breast cancer risk using machine learning involves collecting data on a variety of genetic and demographic factors and using this data to train a machine learning model. These factors might include:
Genetic mutations: Certain genetic mutations, such as those in the BRCA1 and BRCA2 genes, are associated with an increased risk of breast cancer.
Family history: A family history of breast cancer can also increase an individual’s risk.
Age: The risk of breast cancer increases with age.
Hormonal factors: Factors such as menstrual history, use of hormone replacement therapy, and pregnancy history can also affect breast cancer risk.
Lifestyle factors: Certain lifestyle factors, such as diet, exercise, and alcohol consumption, can also impact breast cancer risk.
Once the data has been collected, a machine learning algorithm can be used to analyze the data and identify patterns or relationships that might indicate an increased risk of breast cancer. The model can then be used to make predictions about an individual’s risk based on their specific genetic and demographic profile.
It’s important to note that predicting breast cancer risk is a complex task and no single tool or approach can provide a definitive answer. It’s always important for individuals to discuss their risk with a healthcare provider and to consider a variety of factors when making decisions about their health.
Behravan, H., Hartikainen, J.M., Tengström, M. et al. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning. Sci Rep 10, 11044 (2020). https://doi.org/10.1038/s41598-020-66907-9
Behravan, H., Hartikainen, J.M., Tengström, M. et al. Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls. Sci Rep 8, 13149 (2018). https://doi.org/10.1038/s41598-018-31573-5
If you are interested in a project related to cancer risk and patient outcome prediction, you can consider reaching out to us.
University of Eastern Finland, Institute of Clinical Medicine
Yliopistonranta 1 C, Canthia building, Kuopio, Finland
hamid.behravan[@]uef.fi
+358 50 5139836
Copyright 2023 – Hamid Behravan
