Abdominal ultrasound is a key non-invasive imaging method for diagnosing liver, kidney, and gallbladder diseases, despite its clinical significance, not all individuals can undergo abdominal ultrasonography during routine health check-ups due to limitations in equipment, cost, and time. This study aims to use basic physical examination data to predict the risk of diseases of the liver, kidney, and gallbladder that can be diagnosed via abdominal ultrasound. Basic physical examination data contain gender, age, height, weight, BMI, pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, fasting blood glucose (FBG), and uric acid—we established seven single-label predictive models and one multi-label predictive model. These models were specifically designed to predict a range of abdominal diseases. The single-label models, utilizing the XGBoost algorithm, targeted diseases such as fatty liver (with an Area Under the Curve (AUC) of 0.9344), liver deposits (AUC: 0.8221), liver cysts (AUC: 0.7928), gallbladder polyps (AUC: 0.7508), kidney stones (AUC: 0.7853), kidney cysts (AUC: 0.8241), and kidney crystals (AUC: 0.7536). Furthermore, a comprehensive multi-label model, capable of predicting multiple conditions simultaneously, was established by FCN and achieved an AUC of 0.6344. We conducted interpretability analysis on these models to enhance their understanding and applicability in clinical settings. The insights gained from this analysis are crucial for the development of targeted disease prevention strategies. This study represents a significant advancement in utilizing physical examination data to predict ultrasound results, offering a novel approach to early diagnosis and prevention of abdominal diseases.

中文翻译：

---

使用体检数据集基于机器学习的腹部疾病预测模型

腹部超声是诊断肝、肾、胆疾病的重要无创影像检查方法，尽管具有重要的临床意义，但由于设备、费用和时间的限制，并非所有个体都能在常规健康检查中进行腹部超声检查。本研究旨在利用基本体检数据来预测可通过腹部超声诊断的肝脏、肾脏和胆囊疾病的风险。基本体检数据包含性别、年龄、身高、体重、BMI、脉搏、收缩压（SBP）、舒张压（DBP）、高密度脂蛋白（HDL）、低密度脂蛋白（LDL）、总胆固醇、甘油三酯、空腹血糖 (FBG) 和尿酸——我们建立了七个单标签预测模型和一个多标签预测模型。这些模型是专门为预测一系列腹部疾病而设计的。单标签模型利用 XGBoost 算法，针对脂肪肝（曲线下面积 (AUC) 为 0.9344）、肝脏沉积物（AUC：0.8221）、肝囊肿（AUC：0.7928）、胆囊息肉（AUC：0.7928）等疾病（ AUC：0.7508）、肾结石（AUC：0.7853）、肾囊肿（AUC：0.8241）和肾晶体（AUC：0.7536）。此外，FCN 建立了一个能够同时预测多种条件的综合多标签模型，其 AUC 为 0.6344。我们对这些模型进行了可解释性分析，以增强它们在临床环境中的理解和适用性。从该分析中获得的见解对于制定有针对性的疾病预防策略至关重要。这项研究代表了利用体检数据预测超声结果的重大进步，为腹部疾病的早期诊断和预防提供了一种新方法。