The output of the MLR model.
包含沙特阿拉伯1000名2型糖尿病患者的人口统计、生活方式和血脂数据，用于预测发病年龄的回归模型研究。
基本信息
资源简介
The output of the MLR model.
【数据集背景】
源站首次发布：2025-02-11。
【数据内容】
源站原始描述：The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted at King Abdulaziz Medical University Hospital, aims to predict the age at onset of T2D using Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree Regression (DTR).…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 6.xls：undefined，9.73 KB。数据集 DOI：10.1371/journal.pone.0318484.t005。
【主题与分类】
源站学科分类：Environmental Sciences not elsewhere classified、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：type 2 diabetes、ranks 7th globally、providing valuable tools、mean absolute errors、lipid profile data、key factors influencing、decision tree regression、body mass index、aid early interventions、000 diabetic patients。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/The_output_of_the_MLR_model_/28394817
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Al-hussein, Faten; Tafakori, Laleh; Abdollahian, Mali; Al-Shali, Khalid; Al-Hejin, Ahmed (2025). The output of the MLR model.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：The output of the MLR model.
- DOI：10.1371/journal.pone.0318484.t005
- 发布日期：2025-02-11
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/The_output_of_the_MLR_model_/28394817
命令行下载
curl -L -o "Table 6.xls" "https://ndownloader.figshare.com/files/52285482"
文件清单
Table 6.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted at King Abdulaziz Medical University Hospital, aims to predict the age at onset of T2D using Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree Regression (DTR). It also seeks to identify key predictors influencing the age at onset of T2D in Saudi Arabia, which ranks 7th globally in prevalence. Medical records from 1,000 diabetic patients from 2018 to 2022 that contain demographic, lifestyle, and lipid profile data are used to develop the models. The average onset age was 65 years, with the most common onset range between 40 and 90 years. The MLR and RF models provided the best fit, achieving R2 values of 0.90 and 0.89, root mean square errors (RMSE) of 0.07 and 0.01, and mean absolute errors (MAE) of 0.05 and 0.13, respectively, using the logarithmic transformation of the onset age. Key factors influencing the age at onset included triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), ferritin, body mass index (BMI), systolic blood pressure (SBP), white blood cell count (WBC), diet, and vitamin D levels. This study is the first in Saudi Arabia to employ MLR, ANN, RF, SVR, and DTR models to predict T2D onset age, providing valuable tools for healthcare practitioners to monitor and design intervention strategies aimed at reducing the impact of T2D in the region.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0318484.t005（source: https://plos.figshare.com/articles/dataset/The_output_of_the_MLR_model_/28394817）。
数据缺失？
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