Breast Cancer Recurrence Prediction Using Random Forest Model

Al-Quraishi, Tahsien; Abawajy, Jemal; Chowdhury, Mihir; Rajasegarar, Sutharshan; Abdalrada, Ahmad Shaker

doi:10.1007/978-3-319-72550-5_31

Cited by 20 publications

(10 citation statements)

References 13 publications

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“…RF is highly efficient for handling obscure and unknown data and functions well, even if a dataset is large and complex (Rahmati et al, 2019). RF has been used in many fields and has demonstrated very high forecast accuracy (Chen et al, 2018;Masetic and Subasi, 2018;Hong et al, 2016;Pham et al, 2017;Al-Quraishi et al, 2018;Heddam and Kisi, 2018;Pourghasemi and Rahmati, 2018;Kim et al, 2018;Patel et al, 2019;Hashimoto et al, 2019. It consists of two major internal phases: the algorithm constructs copious bootstrap trials, which are considered calibration sets, and builds categorization conditions for every tree.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Pourghasemi¹,

Pouyan²,

Heidari³

et al. 2020

International Journal of Infectious Diseases

116

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Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. Results: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the

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Section: Random Forest (Rf)mentioning

confidence: 99%

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Pourghasemi¹,

Pouyan²,

Heidari³

et al. 2020

International Journal of Infectious Diseases

116

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“…Among them, the IC I[ 36 ] refers to the weighted average of the absolute average difference between the observed probability and the predicted probability, and can be used to quantify the calibration method in the results of dichotomization, so as to evaluate the calibration effect more comprehensively. As an emerging machine learning algorithm in recent years, random forest mode l[ 37 , 38 ] is a highly flexible classifier containing multiple decision trees. The random forest model solves the shortcoming of the decision tree algorithm, and adopts the random sampling method to enhance the generalization ability.…”

Section: Discussionmentioning

confidence: 99%

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Wang

et al. 2020

BMC Public Health

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Background The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people’s health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome. Methods Design existing circumstances research. A total of 1468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models. Results The results showed that the accuracy of the three models was 82.49,95.98 and 92.03%, the sensitivity was 87.94,95.52 and 90.59%, the specificity was 74.54, 96.65 and 94.14%, the F1 score was 0.86,0.97 and 0.93, and the area under ROC curve was 0.88,0.96 and 0.92, respectively. The Brier score of the three models was 0.15, 0.08 and 0.12, Observed-expected ratio was 0.83, 0.97 and 1.13, and the Integrated Calibration Index was 0.075,0.073 and 0.074, respectively, and explained how the random forest model was used for individual disease risk score. Conclusions The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

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“…As a prediction model for the risk of metabolic syndrome in petroleum workers, the model with higher sensitivity is more suitable for the early detection of patients, so as to play a real role in early detection, early diagnosis and early treatment of the disease, namely secondary prevention of the disease. As an emerging machine learning algorithm in recent years, random forest model [35][36] is a highly exible classi er containing multiple decision trees. The random forest model solves the shortcoming of the decision tree algorithm, and adopts the random sampling method to enhance the generalization ability.…”

Section: Discussionmentioning

confidence: 99%

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Wang

et al. 2020

Preprint

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Abstract Background.The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Objective.To develop and internally verify three risk prediction models for the metabolic syndrome of petroleum workers, compare the prediction performance of the three models, and find the optimal model.Methods. Design existing circumstances research. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results.The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, the area under the ROC curve was 0.894, 0.987 and 0.935, and the Integrated Calibration Index was 0.074, 0.071 and 0.078, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, the area under the ROC curve was 0.797, 0.861 and 0.855, and the Integrated Calibration Index was 0.064, 0.051 and 0.096, respectively.Conclusions.The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

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Breast Cancer Recurrence Prediction Using Random Forest Model

Cited by 20 publications

References 13 publications

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

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