Optimized ensemble model for accurate prediction of cardiac vascular calcification in diabetic patients.

IF 3.1 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
M Suresh, M Maragatharajan
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引用次数: 0

Abstract

Aim: Cardiovascular diseases (CVD) are a major threat to diabetic patients, with cardiac vascular calcification (CVC) as a key predictive factor. This study seeks to improve the prediction of these calcifications using advanced machine learning (ML) algorithms. However, current ML and Artificial Intelligence (AI) methods face challenges such as limited sample sizes, insufficient data, high time complexity, long processing times, and significant implementation costs.

Method: To predict CVC in diabetic patients, the Simple linear iterative clustering based Ensemble Artificial Neural Network (SLIC-EANN) model is proposed in this paper. In this research article, certain biochemical, imaging, and clinical data are used that are captured from Coronary computed tomography angiography (CCTA) dataset. The proposed model employs preprocessing techniques such as image normalization, image resizing, and image augmentation to clean and simplify the input images. Then Localization of the cardiac vascular calcification is done using the simple linear iterative clustering (SLIC) algorithm. The ensemble artificial neural network (EANN) classifies calcification severity by integrating outputs from three machine learning techniques Support Vector Machine (SVM), Gradient Boosting (GB), and Decision Tree (DT).

Results: This method achieves an accuracy of 98.7% and an error rate of 1.3%, outperforming existing techniques.

Conclusion: A comprehensive analysis is conducted in this research article that concludes that the proposed model achieved better prediction performances of calcification in diabetic patients.

优化集成模型准确预测糖尿病患者心血管钙化。
目的:心血管疾病(CVD)是糖尿病患者的主要威胁,而心血管钙化(CVC)是糖尿病患者的重要预测因素。本研究旨在利用先进的机器学习(ML)算法改进这些钙化的预测。然而,当前的机器学习和人工智能(AI)方法面临着诸如样本量有限、数据不足、时间复杂性高、处理时间长和实施成本高等挑战。方法:提出基于简单线性迭代聚类的集成人工神经网络(SLIC-EANN)模型预测糖尿病患者CVC。在这篇研究文章中,使用了从冠状动脉计算机断层血管造影(CCTA)数据集中捕获的某些生化、成像和临床数据。该模型采用图像归一化、图像大小调整和图像增强等预处理技术对输入图像进行清理和简化。然后采用简单线性迭代聚类(SLIC)算法对血管钙化进行定位。集成人工神经网络(EANN)通过整合支持向量机(SVM)、梯度增强(GB)和决策树(DT)三种机器学习技术的输出来对钙化严重程度进行分类。结果:该方法准确率为98.7%,错误率为1.3%,优于现有技术。结论:本文通过综合分析,认为本文提出的模型对糖尿病患者钙化有较好的预测效果。
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
Acta Diabetologica
Acta Diabetologica 医学-内分泌学与代谢
CiteScore
7.30
自引率
2.60%
发文量
180
审稿时长
2 months
期刊介绍: Acta Diabetologica is a journal that publishes reports of experimental and clinical research on diabetes mellitus and related metabolic diseases. Original contributions on biochemical, physiological, pathophysiological and clinical aspects of research on diabetes and metabolic diseases are welcome. Reports are published in the form of original articles, short communications and letters to the editor. Invited reviews and editorials are also published. A Methodology forum, which publishes contributions on methodological aspects of diabetes in vivo and in vitro, is also available. The Editor-in-chief will be pleased to consider articles describing new techniques (e.g., new transplantation methods, metabolic models), of innovative importance in the field of diabetes/metabolism. Finally, workshop reports are also welcome in Acta Diabetologica.
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