Objective: To develop a predictive model of the total length of the small intestine to be applied in bariatric surgery, allowing for the individualization of surgery for each patient.

Methods: Two Excel tables were generated from a Filemaker file. Python was used through a Notebook format in Google Collaborator. The methodology included data transformation and scaling (MinMaxScaler), clustering (unsupervised machine learning with KMeans), data interpolation (oversampling machine learning technique SMOTE), modeling (PyCaret model - XGBoost), and validation.

Results: The study sample included 1090 cases. Three clusters were obtained to categorize the dataset: low, medium, and high length. The algorithm detected patients in cluster c0 with 62% accuracy and 74% sensitivity, in cluster c1 with 63% accuracy and 50% sensitivity, and in cluster c2 with 86% accuracy and 87% sensitivity. Validation was conducted with a new sample of 54 cases, showing results of 50% accuracy and 42% sensitivity for cluster c0, 58% accuracy and 61% sensitivity for cluster c1, and 30% accuracy and 43% sensitivity for cluster c2.

Conclusions: The development of a predictive algorithm for estimating the total length of the small intestine using clustering and machine learning techniques, along with XGBoost classification, is feasible, applicable, and potentially improvable with more data, both in terms of patient numbers and variables to consider.

Keywords: Cirugía bariátrica; Clustering; Inteligencia artificial; Machine learning; Modelo predictivo; artificial intelligence; bariatric surgery; clustering; machine learning; predictive model.

Copyright © 2025. Published by Elsevier España, S.L.U.

目标：开发一个用于预测小肠总长度的模型，以应用于减肥手术，并允许为每位患者个性化手术。

方法：从 Filemaker 文件生成了两个 Excel 表格。通过 Google Collaborator 的 Notebook 格式使用 Python 进行分析。该方法包括数据转换和缩放（MinMaxScaler）、聚类（无监督机器学习 KMeans）、数据插值（oversampling 机器学习技术 SMOTE）、建模（PyCaret 模型 - XGBoost）以及验证。

结果：研究样本包括了1090个病例。获得了三个聚类来对数据集进行分类：低、中和高长度。算法在 c0 聚类中检测患者的准确率为62%，灵敏度为74%；在 c1 聚类中的准确率为63%，灵敏度为50%；在 c2 聚类中的准确率为86%，灵敏度为87%。使用新样本的54个病例进行验证，显示 c0 聚类的准确率为50%，灵敏度为42%；c1 聚类的准确率为58%，灵敏度为61%；c2 聚类的准确率为30%，灵敏度为43%。

结论：使用聚类和机器学习技术，结合 XGBoost 分类器开发一个用于估计小肠总长度的预测算法是可行且适用的，并有可能通过增加数据量（包括患者数量和考虑变量）进一步改进。