This study examined an artificial intelligence software used in practice to help physicians identify risk factors and determine screening recommendations for breast cancer. The program helps physicians determine if cells under a microscope are benign, malignant, or if they have a chance of becoming malignant. The researchers adjusted various settings in the software to determine which settings were more or less beneficial for analyzing patient healthcare data. ANOVA helps researchers determine if the changes that were made within a model truly made a difference or if they just happened by chance. The researchers found that if carefully fine-tuned, the artificial intelligence program can be a beneficial tool in screening patients for cancer.

The following are the assumptions for ANOVA: All populations involved follow a normal distribution, the groups are mutually exclusive, the groups have the same variance (or standard deviation), the groups are randomly selected and independent of one another, and the dependent variable is measured at the interval or ratio level.

1.All populations involved follow a normal distribution.

The researchers analyzed continuous performance, only done on normally distributed data. The researchers utilized Residual vs Predicted values plots to ensure the data was normally distributed.

2.The groups are mutually exclusive

The researchers ensured outliers were removed from the study to ensure data was not falsely skewed by any outliers.

3.The groups have the same variance (or standard deviation).

-The researchers used Residuals vs Predicted value plots to ensure consistent variance within the dataset.

4.The groups are randomly selected and independent of one another.

The researchers systematically changed settings within the software to see how each change specifically impacted how the software ran/helped deliver patient care.

5.The dependent variable is measured at the interval or ratio level.

The researchers utilized numeric data at the ratio level to analyze the softwares results.

References

Adib-Hajbaghery, M., Lotfi, M. S., Rezaee-Shahsavarloo, Z., & Sadat Mousavi, M. (2014). The incidence of hypertension and the follow-up rate in a sample of population over 30 years old in kashan 2013. Journal of caring sciences, 3(3), 211-219. https://doi.org/10.5681/jcs.2014.023

Respond to above post with short answer

1. Explain why you agree/disagree with your peer's assessment of their supplied technique
   • If you disagree, be specific about why and which parts of their assessment you disagree with.
   • Even if you agree with the interpretation, are there some possible objections to the either the conclusion that is made or the assumptions that made the technique possible