Effective management of colorectal cancer (CRC) necessitates precise prognostication and informed decision-making, yet existing literature often lacks emphasis on parsimonious variable selection and conveying complex interdependencies among factors to medical practitioners. To address this gap, we propose a decision support system integrating Elastic Net (EN) and Simulated Annealing (SA) algorithms for variable selection, followed by Tree Augmented Naive Bayes (TAN) modeling to elucidate conditional relationships. Through k-fold cross-validation, we identify optimal TAN models with varying variable sets and explore interdependency structures. Our approach acknowledges the challenge of conveying intricate relationships among numerous variables to medical practitioners and aims to enhance patient-physician communication. The stage of cancer emerges as a robust predictor, with its significance amplified by the number of metastatic lymph nodes. Moreover, the impact of metastatic lymph nodes on survival prediction varies with the age of diagnosis, with diminished relevance observed in older patients. Age itself emerges as a crucial determinant of survival, yet its effect is modulated by marital status. Leveraging these insights, we develop a web-based tool to facilitate physician–patient communication, mitigate clinical inertia, and enhance decision-making in CRC treatment. This research contributes to a parsimonious model with superior predictive capabilities while uncovering hidden conditional relationships, fostering more meaningful discussions between physicians and patients without compromising patient satisfaction with healthcare provision.

结直肠癌（CRC）的有效管理需要精确的预测和明智的决策，但现有文献往往缺乏对简约变量选择的重视，也没有向医生传达因素之间复杂的相互依赖性。为了解决这一差距，我们提出了一种决策支持系统，集成了弹性网络（EN）和模拟退火（SA）算法来进行变量选择，然后通过树增强朴素贝叶斯（TAN）建模来阐明条件关系。通过 k 折交叉验证，我们确定具有不同变量集的最佳 TAN 模型并探索相互依赖结构。我们的方法承认向医生传达众多变量之间复杂关系的挑战，并旨在加强患者与医生的沟通。癌症分期成为一个强有力的预测因素，其重要性随着转移淋巴结的数量而放大。此外，转移淋巴结对生存预测的影响随诊断年龄的不同而变化，在老年患者中观察到的相关性减弱。年龄本身是生存的关键决定因素，但其影响受到婚姻状况的调节。利用这些见解，我们开发了一种基于网络的工具，以促进医患沟通、减轻临床惰性并增强 CRC 治疗的决策能力。这项研究有助于建立一个具有卓越预测能力的简约模型，同时揭示隐藏的条件关系，促进医生和患者之间更有意义的讨论，同时又不影响患者对医疗服务的满意度。