Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Guixiong Huang, Weilin Zhu, Yulong Wang, Yizhou Wan, Kaifang Chen, Yanlin Su, Weijie Su, Lianxin Li, Pengran Liu, Xiao Dong Guo
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Abstract

Objective: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without osteoporosis. Various machine learning algorithms were employed for training and testing the model, evaluating its performance, and conducting validations to explore the most suitable machine learning algorithm.

Methods: Clinical information, including demographic data, examination results, medical history, and laboratory test results, was collected from inpatients with and without osteoporosis. The LASSO algorithm was utilized for feature selection, and multiple machine learning algorithms were applied to calculate the model's accuracy, precision, recall, F1 score, and average precision (AP) value. Receiver operating characteristic (ROC) curves for each algorithm were plotted, and a comprehensive evaluation was conducted to identify the most suitable machine learning model. Finally, the model's predictive accuracy was validated using corresponding information from other patients.

Results: A total of 1063 patients were included; 562 had osteoporosis, and 501 did not. After LASSO feature selection, the most important features for the model's predictive results were determined to be age, height, weight, alkaline phosphatase activity, and osteocalcin. Evaluation of the accuracy, precision, recall, F1 score, and AP value for each algorithm, along with ROC curves, led to the selection of the light gradient boosting machine (LGBM) algorithm as the best algorithm for the model. The validation results confirmed the model's excellent predictive ability.

Conclusion: This study established a preliminary diagnostic model for osteoporosis, contributing to increased efficiency in diagnosing the disease.

一些机器学习算法可以在训练和测试一些患者的临床信息后识别骨质疏松症患者吗?
目的:通过收集骨质疏松症和非骨质疏松症患者的临床资料,建立骨质疏松症的诊断模型。使用各种机器学习算法对模型进行训练和测试,评估其性能,并进行验证,以探索最合适的机器学习算法。方法:收集有骨质疏松症和无骨质疏松症住院患者的临床资料,包括人口统计资料、检查结果、病史和实验室检查结果。利用LASSO算法进行特征选择,并应用多种机器学习算法计算模型的准确率、精密度、召回率、F1分数和平均精密度(AP)值。绘制每种算法的受试者工作特征(ROC)曲线,并进行综合评估,以确定最合适的机器学习模型。最后,使用其他患者的相应信息验证模型的预测准确性。结果:共纳入1063例患者;562人有骨质疏松症,501人没有。LASSO特征选择后,确定模型预测结果的最重要特征为年龄、身高、体重、碱性磷酸酶活性和骨钙素。通过对每种算法的准确率、精密度、召回率、F1评分和AP值以及ROC曲线的评估,选择光梯度增强机(light gradient boosting machine, LGBM)算法作为模型的最佳算法。验证结果证实了该模型具有良好的预测能力。结论:本研究建立了骨质疏松症的初步诊断模型,有助于提高骨质疏松症的诊断效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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