Death risk and the importance of clinical features in elderly people with COVID-19 using the Random Forest Algorithm

Lima, Tiago Pessoa; Sena, Gabrielle Ribeiro; Neves, Camila Soares; Vidal, Suely Arruda; Lima, Jurema Telles de Oliveira; Mello, Maria Julia Gonçalves; Orange, Flávia Augusta de

doi:10.1590/1806-9304202100s200007

Cited by 5 publications

(7 citation statements)

References 16 publications

(14 reference statements)

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“…If the three make distinct errors, then when h1(xi)is wrong, it is possible that h2 (xi) and h3 (xi) are correct, so that the combination of hypotheses by voting can correctly rank xi. (LIMA et al, 2021). Classification via randomized forest or RF algorithm is based on the ensemble strategy, i.e.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Classification Algorithms: Gaussian Mixture Model - GMM and Random Forest - RF, for Landsat 8 Images

Pantoja¹,

Spenassato²,

Emmendorfer³

2023

RGSA

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Purpose: Given the importance of monitoring and managing land cover, especially in countries with continental proportions, such as Brazil. This research aimed to compare two remote sensing image classifier algorithms. Method/design/approach: The article compared the Gaussian Mixture Model and Random Forest classification algorithms, using Landsat 8 image, which was classified in a supervised way, in the Dezetsaka plugin of QGIS. The analysis of the performance of each model was performed using the Kappa index and Total Accuracy. Results and conclusion: The results showed that the Random Forest algorithm was more efficient than the Gaussian Mixture Model. Taking the Kappa Index (K) and Total Accuracy (po), the models obtained the following performances in the classification of classes: the Random Forest Model (K= 0.94 and po= 96.31) and the Gaussian Mixture Model obtained (K=0.85 and po=90.60). Research implications: The results can support the choice of classification method by researchers and others interested in monitoring land cover. Originality/value: This is a unique proposal, which compares an algorithm based on Machine Learning with another one from the category of probabilistic models. Interesting, since machine learning techniques have been gaining notoriety in several contexts.

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Section: Introductionmentioning

confidence: 99%

Comparison Between Classification Algorithms: Gaussian Mixture Model - GMM and Random Forest - RF, for Landsat 8 Images

Pantoja¹,

Spenassato²,

Emmendorfer³

2023

RGSA

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show abstract

“…Cross‐validation ( K ‐fold cross validation) is a sampling method used to analyse the performance of ML algorithms. Cross‐validation consists of randomly dividing the data into mutually exclusive K folds of equal sizes 35 …”

Section: Methodsmentioning

confidence: 99%

“…This algorithm generates several decision trees, and each tree is trained with a random distribution. A major advantage of the RF is the ease of measuring the relative importance of each attribute for the prediction by analysing how many nodes in the trees use a given attribute to reduce the overall impurity of the forest 35 . The RF model was built through the randomForest function, which is a part of the package with the same name.…”

Section: Methodsmentioning

confidence: 99%

“…A major advantage of the RF is the ease of measuring the relative importance of each attribute for the prediction by analysing how many nodes in the trees use a given attribute to reduce the overall impurity of the forest. 35 The RF model was built through the randomForest function, which is a part of the package with the same name. The analysis was performed using the following established basic parameters: number of trees (ntrees) = 500, minimum size of terminal nodes (nodesize) = 5 and number of variables used in each tree (mtry) = 6.…”

Section: Ensemble Algorithmsmentioning

confidence: 99%

“…Crossvalidation consists of randomly dividing the data into mutually exclusive K folds of equal sizes. 35 SVMs SVM classifiers operate by separating classes using linear decision boundaries called hyperplanes. 36 The model is a representation of mapped points such that the examples of each category are divided by a clear and well-defined space.…”

Section: Clustering Analysismentioning

confidence: 99%

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Clustering analysis and machine learning algorithms in the prediction of dietary patterns: Cross‐sectional results of the Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil)

Silva

Gorgulho

Marchioni

et al. 2022

J Human Nutrition Diet

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Background Machine learning investigates how computers can automatically learn. The present study aimed to predict dietary patterns and compare algorithm performance in making predictions of dietary patterns. Methods We analysed the data of public employees (n = 12,667) participating in the Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil). The K‐means clustering algorithm and six other classifiers (support vector machines, naïve Bayes, K‐nearest neighbours, decision tree, random forest and xgboost) were used to predict the dietary patterns. Results K‐means clustering identified two dietary patterns. Cluster 1, labelled the Western pattern, was characterised by a higher energy intake and consumption of refined cereals, beans and other legumes, tubers, pasta, processed and red meats, high‐fat milk and dairy products, and sugary beverages; Cluster 2, labelled the Prudent pattern, was characterised by higher intakes of fruit, vegetables, whole cereals, white meats, and milk and reduced‐fat milk derivatives. The most important predictors were age, sex, per capita income, education level and physical activity. The accuracy of the models varied from moderate to good (69%–72%). Conclusions The performance of the algorithms in dietary pattern prediction was similar, and the models presented may provide support in screener tasks and guide health professionals in the analysis of dietary data.

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Modelo preditivo de infecção hospitalar utilizando aprendizado de máquina

Mendes¹

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Death risk and the importance of clinical features in elderly people with COVID-19 using the Random Forest Algorithm

Cited by 5 publications

References 16 publications

Comparison Between Classification Algorithms: Gaussian Mixture Model - GMM and Random Forest - RF, for Landsat 8 Images

Comparison Between Classification Algorithms: Gaussian Mixture Model - GMM and Random Forest - RF, for Landsat 8 Images

Clustering analysis and machine learning algorithms in the prediction of dietary patterns: Cross‐sectional results of the Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil)

Modelo preditivo de infecção hospitalar utilizando aprendizado de máquina

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