用于高胰岛素血症诊断的可解释数据挖掘模型

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-03-04 DOI:10.1080/09540091.2024.2325496

Nevena Rankovic, Dragica Rankovic, Mirjana Ivanovic, Igor Lukic

引用次数: 0

摘要

在我们的研究中，我们提出了一个数据挖掘模型，用于早期诊断高胰岛素血症，从而降低患糖尿病、心脏病和其他慢性疾病的风险。该数据集收集了...

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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Explainable data mining model for hyperinsulinemia diagnostics

In our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gat...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.