Optimal Time Intervals Between Personal Mammogram Test Decisions
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DOI:
https://doi.org/10.59287/ijanser.650Keywords:
Decision Models, Mammogram Testing, Cancer, Utility Theory, Decision Trees, Bayes TheoremAbstract
Excluding skin cancer, breast cancer is the most common type of cancer in women in the United States, accounting for one in three diagnosed cases. A woman's chance of developing invasive breast cancer in her lifetime is approximately 1 in 8 (12%). While mammography has been effective in early detection, its use has resulted in a minor increase in the number of in situ cancers detected. However, mammography carries potential risks, such as exposure to unnecessary radiation, additional costs, psychological stress, and the possibility of false-positive results. Although the American Cancer Society recommends annual testing, it may be unnecessary for healthy women. In this paper, we aim to find the optimal interval between mammogram tests, balancing the benefits and risks of testing while reducing the false-positive rate and number of tests. To achieve this, we use decision trees, utility theory, and Bayes' theorem to calculate the quality-adjusted life years (QALYs) of patients.
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