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相关概念视频

Aggregates Classification01:29

Aggregates Classification

970
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
970
Classification of Systems-I01:26

Classification of Systems-I

552
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
552
Randomized Experiments01:13

Randomized Experiments

8.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.9K
Survival Tree01:19

Survival Tree

389
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...
389
Random Sampling Method01:09

Random Sampling Method

14.1K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
14.1K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

42.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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相关实验视频

Updated: Jan 18, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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胆结石分类使用随机森林优化沙猫群优化算法与SHAP和基于DiCE的可解释性.

Proshenjit Sarker1, Jun-Jiat Tiang2, Abdullah-Al Nahid1

  • 1Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个优化的随机森林分类器,使用沙猫群优化用于从临床数据中预测胆结石. 该模型有效地识别了CRP,维生素D和AAST等关键指标,改善了诊断潜力.

关键词:
这就是 DiCECE.这就是 SHAP SHAP 的意思.沙猫群集优化 沙猫群集优化胆结石 胆结石是一种胆结石.机器学习是机器学习.随机森林分类器随机森林分类器

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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相关实验视频

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习
  • 计算生物学 计算生物学

背景情况:

  • 胆结石病影响全球10-20%的成年人,需要早期诊断.
  • 机器学习 (ML) 对胆结石检测有希望,但表式数据方法尚未得到充分探索.
  • 用临床数据优化诊断模型对于有效的胆结石管理至关重要.

研究的目的:

  • 开发和评估一种随机森林 (RF) 分类器,该分类器与沙猫群优化 (SCSO) 进行了优化,用于使用表式临床数据预测胆结石.
  • 评估交叉验证和特征减少对模型性能的影响.
  • 识别关键的预测特征并提高模型的可解释性.

主要方法:

  • 一个随机森林 (RF) 分类器使用沙猫群优化 (SCSO) 算法被开发和优化.
  • 实验使用四个框架进行:没有交叉验证的RF (CV),带有CV的RF,没有CV的RF-SCSO和带有CV的RF-SCSO.
  • 使用SHAP分析了特征的重要性,使用DiCE增强了模型的解释性.

主要成果:

  • 射频-SCSO模型实现了与标准射频模型相似的准确性,同时显著减少了功能集从38到13.
  • SHAP分析确定了C反应蛋白 (CRP),维生素D和阿斯巴胺转移酶 (AAST) 作为具有高度影响力的特征.
  • 对于错误分类的实例,DiCE分析提供了纠正的反事实,提高了模型的透明度.

结论:

  • 优化的ML模型,特别是RF-SCSO,通过结构化临床数据为胆结石诊断提供了可行的方法.
  • 像SHAP和DiCE这样的特征选择和可解释性方法对于完善诊断ML模型是有价值的.
  • 这项研究强调了表格数据和可解释的ML在改善胆结石疾病管理方面的潜力.