2022
DOI: 10.1186/s12911-022-01939-x
|View full text |Cite
|
Sign up to set email alerts
|

Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran

Abstract: Background Due to the high mortality of COVID-19 patients, the use of a high-precision classification model of patient’s mortality that is also interpretable, could help reduce mortality and take appropriate action urgently. In this study, the random forest method was used to select the effective features in COVID-19 mortality and the classification was performed using logistic model tree (LMT), classification and regression tree (CART), C4.5, and C5.0 tree based on important features. … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
7
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 18 publications
(7 citation statements)
references
References 36 publications
0
7
0
Order By: Relevance
“…This method removes the subtree when the computed error is high. This method is more efficient and yields superior outcomes ( Moslehi et al, 2022 ).…”
Section: Methodsmentioning
confidence: 99%
“…This method removes the subtree when the computed error is high. This method is more efficient and yields superior outcomes ( Moslehi et al, 2022 ).…”
Section: Methodsmentioning
confidence: 99%
“…One of the most popular ways to use decision trees is to present the findings as a straightforward decision tree method that is basic enough for most researchers to understand. The decision trees are capable of non-parametric adjustment, control over heterogeneous data, and the best classification of consecutive data even if features are not normalized and scaled [11]. A large number of decision trees are generated and combined to form a 'forest' using a complex and flexible supervised machine learning method known as Random Forest [14].…”
Section: Methodsmentioning
confidence: 99%
“…In the field of Machine Learning (ML), computers are trained to learn from examples of data and analyses data processing experience instead of being explicitly programmed to perform predetermined tasks. Examples of algorithms include the extreme gradient boosting, the support vector machine, and the random forest algorithm [11]. Rules that precisely specify a series of operations are known as algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…Capable of fitting intricate datasets, these algorithms are incredibly strong [23]. For researches on medical application [24][25][26], road safety [27][28][29], to manage and analyze the data on metal nanoparticles [30], DT methodology is becoming very popular. DTs require very little data preparation, which is only one of their numerous benefits.…”
Section: Decision Treementioning
confidence: 99%