  This research paper investigates the effectiveness of simple linear models
versus complex machine learning techniques in breast cancer diagnosis,
emphasizing the importance of interpretability and computational efficiency in
the medical domain. We focus on Logistic Regression (LR), Decision Trees (DT),
and Support Vector Machines (SVM) and optimize their performance using the UCI
Machine Learning Repository dataset. Our findings demonstrate that the simpler
linear model, LR, outperforms the more complex DT and SVM techniques, with a
test score mean of 97.28%, a standard deviation of 1.62%, and a computation
time of 35.56 ms. In comparison, DT achieved a test score mean of 93.73%, and
SVM had a test score mean of 96.44%. The superior performance of LR can be
attributed to its simplicity and interpretability, which provide a clear
understanding of the relationship between input features and the outcome. This
is particularly valuable in the medical domain, where interpretability is
crucial for decision-making. Moreover, the computational efficiency of LR
offers advantages in terms of scalability and real-world applicability. The
results of this study highlight the power of simplicity in the context of
breast cancer diagnosis and suggest that simpler linear models like LR can be
more effective, interpretable, and computationally efficient than their complex
counterparts, making them a more suitable choice for medical applications.
