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.

研究了简单线性模型与复杂机器学习技术在乳腺癌诊断中的有效性，强调了解释性和计算效率在医学领域的重要性。结果表明，相对于决策树和支持向量机，简单线性模型具有更好的性能，可提供更清晰的输出和更高的可扩展性和实用性。

简洁的力量：为何简单线性模型胜过复杂机器学习技术——以乳腺癌诊断为例