Imagine you’re trying to predict the weather. You look at factors like cloud cover and temperature, but you’re not sure if they’re reliable indicators of rain.
A new study did something similar for breast cancer, examining sleep and depression as potential “indicators.”
It found that while the amount of sleep might not tell us much about breast cancer risk, being depressed could be a warning sign. Also, by using machine learning algorithms, researchers developed a way to better predict breast cancer risk, similar to how meteorologists in our analogy can predict the weather with advanced models.
The study delves into the complex relationship between sleep duration, depression, and breast cancer among women, utilizing data from a large, national survey and advanced machine learning techniques for analysis.
The team focused on two potential factors or links to breast cancer risk: sleep duration and depression. Previous research has been mixed, leaving it unclear how, or if, these factors relate to breast cancer risk.
Researchers analyzed data from 1,789 women, including 263 who had been diagnosed with breast cancer, from the National Health and Nutrition Examination Survey (NHANES). They used a questionnaire to measure sleep duration and a health questionnaire (PHQ-9) to assess levels of depression. Then, they applied six different machine learning algorithms to try to predict breast cancer occurrence, aiming to find the most accurate one.
Below are some of the key findings:
- Depression: Women with depression were more likely to have breast cancer. The numbers showed a significant association, suggesting that depression might be a risk factor for developing breast cancer.
- Sleep Duration: Contrary to what one might expect, the study found no clear link between how long women slept and their breast cancer risk. Whether women slept less than 7 hours, the recommended 7-9 hours, or more than 9 hours, it didn’t significantly affect their likelihood of having breast cancer.
The study concluded that while sleep duration didn’t significantly affect breast cancer risk, depression did. Moreover, machine learning, particularly the AdaBoost algorithm, showed promise in predicting breast cancer risk.
This insight could help in early detection and treatment strategies, though the researchers caution that more studies are needed to understand these relationships better.
