The random forest algorithm for statistical learning

Schonlau, Matthias; Zou, Rosie Yuyan

doi:10.1177/1536867x20909688

Cited by 546 publications

(259 citation statements)

References 11 publications

(8 reference statements)

Supporting

Mentioning

256

Contrasting

Unclassified

Order By: Relevance

“…P values of < 0•05 were considered statistically significant. Such aggregation was computed by iteratively using the Boruta algorithm [21] and a random forest classifier [22].…”

Section: Discussionmentioning

confidence: 99%

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab

et al. 2020

View full text Add to dashboard Cite

Objectives We aimed to assess the diagnostic performance of CT in patients with a negative first RT-PCR testing and to identify typical features of COVID-19 pneumonia that can guide diagnosis in this case. Methods Patients suspected of COVID-19 with a negative first RT-PCR testing were retrospectively revalued after undergoing CT. CT was reviewed by two radiologists and classified as suspected COVID-19 pneumonia, non-COVID-19 pneumonia or negative. The performance of both first RT-PCR result and CT was evaluated by using sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) and by using the second RT-PCR test as the reference standard. CT findings for confirmed COVID-19 positive or negative were compared by using the Pearson chi-squared test (P values < 0.05) Results Totally, 337 patients suspected of COVID-19 underwent CT and nasopharyngeal swabs in March 2020. Eighty-seven out of 337 patients had a negative first RT-PCR result; of these, 68 repeated RT-PCR testing and were included in the study.

show abstract

“…P values of < 0•05 were considered statistically significant. Such aggregation was computed by iteratively using the Boruta algorithm [21] and a random forest classifier [22].…”

Section: Discussionmentioning

confidence: 99%

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab

et al. 2020

View full text Add to dashboard Cite

show abstract

“…First, we used a random forest algorithm. 13 A random forest is composed of multiple individual decision trees that operate as an ensemble (online supplementary material). To derive the random forest, we split the data randomly into a 50% partition used for training and 50% used for validation.…”

Section: Methodsmentioning

confidence: 99%

Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

et al. 2020

View full text Add to dashboard Cite

ObjectiveElectrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is difficult. The objective was to develop sex-specific prediction models for successful electrical cardioversion and assess the potential of machine learning methods in comparison with traditional logistic regression.MethodsIn a retrospective cohort study, we examined several candidate predictors, including comorbidities, biochemistry, echocardiographic data, and medication. The outcome was successful cardioversion, defined as normal sinus rhythm immediately after the electrical cardioversion and no documented recurrence of AF within 3 months after. We used random forest and logistic regression models for sex-specific prediction.ResultsThe cohort comprised 332 female and 790 male patients with persistent AF who underwent electrical cardioversion. Cardioversion was successful in 44.9% of the women and 49.9% of the men. The prediction errors of the models were high for both women (41.0% for machine learning and 48.8% for logistic regression) and men (46.0% for machine learning and 44.8% for logistic regression). Discrimination was modest for both machine learning (0.59 for women and 0.56 for men) and logistic regression models (0.60 for women and 0.59 for men), although the models were well calibrated.ConclusionsSex-specific machine learning and logistic regression models showed modest predictive performance for successful electrical cardioversion. Identifying patients who will benefit from cardioversion remains challenging in clinical practice. The high recurrence rate calls for thoroughly informed shared decision-making for electrical cardioversion.

show abstract

“…This process is shown in Figure 2. The result brings in more accurate estimates of error rate than the decision trees 30 …”

Section: Random Forest Techniquementioning

confidence: 99%

Development and application of random forest technique for element level structural damage quantification

Chencho

Hao

et al. 2020

Struct Control Health Monit

View full text Add to dashboard Cite

Summary This paper presents element level structural damage quantification using an ensemble‐based machine learning technique, namely, random forest technique, with acceleration responses from structures. The ensemble‐based approach provides a better prediction than an individual model. Random forest is a machine learning algorithm which has several decision trees to perform a task. The proposed approach develops a random forest as a regressor to predict multiple output variables, which is the vector of elemental level damage quantification results of the structure. Damage severity is identified as the reduction in the elemental stiffness parameters. The acceleration responses for single‐element and multiple element damage cases are generated and further processed to feed as input to the random forest. The acceleration responses from the sensor nodes are concatenated, and principal component analysis (PCA) is applied to reduce the uncorrelated input dimension. The proposed approach can provide good damage identification results efficiently with much less computational demand and time, compared to the neural network training methods and deep learning models. Moreover, fewer sensors are needed to measure acceleration responses to localise damage and quantify the damage level. To demonstrate the proposed method, a simply supported beam is used as an example in numerical studies. Different levels of noise in the acceleration responses and uncertainty in the finite element modelling are considered. Experimental studies on a steel frame structure with 70 elements are also conducted to investigate the performance of the proposed approach for structural damage quantification. Good identification results are obtained with an efficient training process.

show abstract

The random forest algorithm for statistical learning

Cited by 546 publications

References 11 publications

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab

Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

Development and application of random forest technique for element level structural damage quantification

Contact Info

Product

Resources

About