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Updated: May 10, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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随机森林算法用于预测26个撒哈拉以南非洲国家的孕妇的烟草使用和确定决定因素:2024年分析

Eliyas Addisu Taye1, Eden Yitbarek Woubet2, Gabrela Yimer Hailie3

  • 1Department of Health Informatics, Institute of Public Health, University of Gondar, Gondar, Ethiopia. eliyasaddisu12@gmail.com.

BMC public health
|April 24, 2025
PubMed
概括

机器学习准确地预测了撒哈拉以南非洲孕妇的烟草使用情况. 关键因素包括教育,财富和获得医疗保健,这表明需要政策干预.

关键词:
机器学习 机器学习预测 预测 预测随机森林分类器是随机森林分类器.这就是 SHAP SHAP 的意思.撒哈拉以南非洲地区.使用烟草使用烟草.

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Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke09:50

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相关概念视频

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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