Samaneh Rikhtehgaran

Ph.D.

Florida Atlantic University

Graduate Student

PLCO (Learn more about this study)

PLCO-777

Apr 21, 2021

Efficient machine learning algorithms for investigating and comparing the risk factors of breast and prostate cancers among males and females

Breast and prostate cancers are among the most common invasive cancers in the world. Although, these two cancers are different they share similarities in some aspects. Breast cancer is the leading of cancer in terms of the incidence among women worldwide. In breast cancer, the most important risk factor is probably family history. Therefore, the breast cancer risk increases with the number of first-degree relatives with the disease. Risk factors other than family history are age, race, smoking, breast tissue density, weight and alcohol consumption. On the other hand, prostate cancer is the third leading cause of cancer death in North America. Prostate cancer risk increases with the number of relatives diagnosed with this cancer and decreases with the relative’s age at diagnosis. Other factors than family history can be listed as age, ethnicity and genetic factors. In this project, we use several machine learning algorithms such as Logistic Regression, Decision Trees, and etc. to investigate risk factors of breast and prostate cancers among males and females and also to compare these risk factors in order to find the possible similarities or differences. For this reason, we would like to use PLCO dataset. Comprehensive comparison of risk factors leading to breast and prostate cancers can have a huge impact on early detection and progressive improvement in survival.