Penalized semiparametric Cox regression model on XGBoost and random survival forests

Wang, Yating; Su, Jinxia; Zhao, Xuejing

doi:10.1080/03610918.2021.1926505

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Lately, several machine learning (ML) algorithms have been developed to overcome the shortcomings of statistical models, such as high dimensionality and nonlinearities (43)(44)(45). Of these, gradient boosted algorithms are used in several works, often in combination with various feature selection techniques, with satisfactory performances (43,(46)(47)(48)(49)(50). Considering specific applications for NSCLC, a systematic review and metanalysis by Kothari et al has recently provided a state-of-art representation of radiomics for this subset of patients (33).…”

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confidence: 99%

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Volpe

Isaksson

Zaffaroni

et al. 2022

Transl Lung Cancer Res

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Background: No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed.Methods: Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded.Cox PH with LASSO regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low-and high-volume lesions.Results: Radiomic models significantly outperformed models built on only clinical variables and volume.However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models.Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low-and high-volume models performed significantly better with the inclusion of *, affiliation at the time of the study.

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confidence: 99%

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Volpe

Isaksson

Zaffaroni

et al. 2022

Transl Lung Cancer Res

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“…where, denotes the predicted value of the model; K denotes the number of decision trees; denotes the kth sub-model; denotes the i-th input sample; and F denotes the set of all decision trees [31]. The objective function of XGBoost consists of two components, the loss function and the canonical term:…”

Section: Defect Estimation Methodsmentioning

confidence: 99%

A pipeline defect estimation method based on supervised learning fusion model

Li²,

Wang

et al. 2022

Preprint

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Pipeline health assessment is an important work in industry, and information on the type and size of defects is an essential basis for assessing the health of a pipeline. Therefore, a pipeline defect estimation method based on supervised learning ensemble model is proposed in this paper. Firstly, several typical feature factors are calculated using feature formulas in the field of acoustics, capable of distinguishing the defect signal variability. Thereafter, Pearson correlation coefficient analysis and Random Forest importance ranking feature analysis methods are utilized to filter out the more valuable features. To improve the performance of defect estimation, a fusion model combining qualitative and quantitative analysis based on Random Forest and XGBoost is constructed to preferentially identify the type of defect signal qualitatively and then predict the size quantitatively. Finally, experimental results and comprehensive analysis with other mainstream supervised learning methods indicate that the prediction error of this method is basically below 1.5%, which addresses the issue of the low estimation accuracy of traditional methods.

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“…In summary, the main contributions [9][10][11] of the research examined in this question are as follows:…”

Section: Introductionmentioning

confidence: 99%

Wordle game based on time series prediction

Zhu,

Shan,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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The wordle game offered daily by the New York Times has taken the United States by storm and is loved by many people and challenged by major internet users. In order to improve the user gaming experience, it is therefore necessary to analyse the game user data. This paper uses a time series model, a random forest regression model, and a random forest classification model, and constructs eight attribute features for the words, predicts the difficulty of guessing EERIE words as being difficult based on the attribute features, and visualises other features of the data. Firstly, the ARIMA(1,1,0) model was used and predicted an approximate prediction interval of [18578, 22562] for the outcome data reported on 1 March 2023. Next, we used a random forest regression model as well as a random forest classification model and predicted the percentage of players who could guess the word EERIE for the first time on March 1, 2023, respectively. The words were categorised by difficulty: easy, average and hard. We finally predicted that the word EERIE belongs to the difficult class. Finally, the following features were created based on the data obtained. The first feature: a time-series plot of the number of reported results; the second feature: a plot of the mean value of the number of attempts from 1 to 7, and the probability of success of the number of attempts by the player is considered to obey a normal distribution. The research in this paper can improve the wordle game to a certain extent, enhance the players' gaming experience and attract more people to participate in the wordle game.

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Penalized semiparametric Cox regression model on XGBoost and random survival forests

Cited by 8 publications

References 11 publications

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

A pipeline defect estimation method based on supervised learning fusion model

Wordle game based on time series prediction

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