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从2013年到2019年使用拉丁方形提取和模糊人工神经网络预测慢性非传染性疾病的整体模型

Nevena Rankovic¹, Dragica Rankovic², Igor Lukic³

¹Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, Netherlands.

|November 30, 2023

概括

塞尔维亚的慢性非传染性疾病,特别是高血压,在老年女性中最为普遍. 这个研究研究这个研究.

科学领域:

公共卫生公共卫生
流行病学流行病学
医疗信息学医疗信息学

背景情况:

在塞尔维亚使用2013年和2019年国家数据追踪了17种慢性非传染性疾病 (NCD) 的流行情况.
分析考虑区域,年龄和性别人口统计数据,以确定高风险人群.
旨在为弱势群体提供针对性的,免费的预防性查信息.

研究的目的:

分析塞尔维亚慢性非传染性疾病 (NCD) 的趋势.
确定特定的人口群体和受非传染性疾病影响最严重的地区.
开发和验证一个NCD流行率的预测模型.

主要方法:

利用了来自两项涉及27,801名参与者的国家横截面研究 (2013年,2019年) 的数据.
比较决策树,支持向量回归器和人工神经网络 (ANN) 模型.
实现了一种新的ANN-L36模型,采用塔古奇的强大的设计优化和Fuzzy编码.

主要成果:

心血管疾病,尤其是高血压,是塞尔维亚最常见的非传染性疾病.
在所有地区,高血压在64-75岁的女性中最常见.
从2013年 (34.0%) 到2019年 (32.2%) 观察到高血压患病率略有下降.
使用 Fuzzy 编码的 ANN-L36 模型实现了高精度 (0.1% 的相对误差).

结论:

本研究介绍了塞尔维亚首次在国家一级追踪慢性疾病的情况.
开发的模型为塞尔维亚未来的非传染性疾病医疗保健框架做出了重大贡献.
塔古奇的优化方法对于识别各种慢性非传染性疾病是有效的.

关键词:

ANN-L36模糊模型的模糊模型慢性非传染性疾病慢性非传染性疾病决策树决策树是一个决策树.正交向量平面是正交向量的平面.支持向量的回归器.

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