Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy

Cheng, Zhi; Nakatsugawa, Munehide; Hu, Chen; Robertson, Scott P.; Hui, Xuan; Moore, Joseph A.; Bowers, Michael R.; Kiess, Ana P.; Page, B.R.; Burns, Lindsay H.; Muse, M.; Choflet, Amanda; Sakaue, Kousuke; Sugiyama, S.; Utsunomiya, Kazuki; Wong, John W.; McNutt, Todd; Quon, Harry

doi:10.1016/j.adro.2017.11.006

Cited by 29 publications

(14 citation statements)

References 44 publications

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“…The CART algorithm is an important decision tree algorithm that lies at the foundation of ML. Moreover, it is also the basis for other powerful ML algorithms like bagged decision trees, random forest and boosted decision trees [8].…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

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Evaluating machine learning algorithms for predicting maize yield under conservation agriculture in Eastern and Southern Africa

Mupangwa

Chipindu²,

Nyagumbo³

et al. 2020

SN Appl. Sci.

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Crop simulation models are widely used as research tools to explore the impact of various technologies and compliment field experimentation. Machine learning (ML) approaches have emerged as promising artificial intelligence alternative and complimentary tools to the commonly used crop production models. The study was designed to answer the following questions: (a) Can machine learning techniques predict maize grain yields under conservation agriculture (CA)? (b) How close can ML algorithms predict maize grain yields under CA-based cropping systems in the highlands and lowlands of Eastern and Southern Africa (ESA)? Machine learning algorithms could predict maize grain yields from conventional and CA-based cropping systems under low and high potential conditions of the ESA region. Linear algorithms (LDA and LR) predicted maize yield more closely to the observed yields compared with nonlinear tools (NB, KNN, CART and SVM) under the conditions of the reported study. However, the KNN algorithm was comparable in its yield prediction to the linear tools tested in this study. Overall, the LDA algorithm was the best tool, and SVM was the worst algorithm in maize yield prediction. Evaluating the performance of different ML algorithms using different criteria is critical in order to get a more robust assessment of the tools before their application in the agriculture sector.

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Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

“…In contrast, nonparametric tests do not make assumptions about the model parameters. The range of random inputs is of greater variation even to unrealistic values, thus leading to poor quality outputs [8,33,37].…”

Section: Prediction Power Of Algorithms In Simulating Maize Yieldmentioning

confidence: 99%

Evaluating machine learning algorithms for predicting maize yield under conservation agriculture in Eastern and Southern Africa

Mupangwa

Chipindu²,

Nyagumbo³

et al. 2020

SN Appl. Sci.

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“…In this study, 10 different classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Ten classification algorithms used in this paper are SVM (Vadali et al, 2019), Quadratic Discriminant Analysis (QDA) (Siqueira et al, 2017), CART (Cheng et al, 2017), Linear Discriminant Analysis (LDA) (Liu et al, 2016), KNN (Suyundikov et al, 2015), Ensemble (Xiao et al, 2018), ELM (Sachnev et al, 2015), Particle Swarm Optimization-Support Vector Machine (PSO-SVM) (Jiang et al, 2010), Genetic Algorithm-Support Vector Machine (GA-SVM) (Tao et al, 2019), and ABC-SVM (Alshamlan et al, 2016). Thirteen and twenty-one indicators are used as input characteristics, respectively.…”

Section: Correlation Indicators Validation and Escc Differentiation Pmentioning

confidence: 99%

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

et al. 2020

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The diagnosis of the degree of differentiation of tumor cells can help physicians to make timely detection and take appropriate treatment for the patient's condition. In this study, the original dataset is clustered into two independent types by the Kohonen clustering algorithm. One type is used as the development sets to find correlation indicators and establish predictive models of differentiation, while the other type is used as the validation sets to test the correlation indicators and models. In the development sets, thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by the Kohonen clustering algorithm. Thirteen relevant indicators are used as input features and the degree of tumor differentiations is used as output. Ten classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Artificial bee colony-support vector machine (ABC-SVM) predicts better than the other nine algorithms, with an average accuracy of 81.5% for the 10-fold cross-validation. Based on logistic regression and ReliefF algorithm, five models with the greater merit for the degree of differentiation are found in the development sets. The AUC values of the five models are 0.672, 0.628, 0.630, 0.628, and 0.608 (P < 0.05). The AUC values of the five models in the validation sets are 0.753, 0.728, 0.744, 0.776, and 0.868 (P < 0.0001). The predicted values of the five models are constructed as the input features of ABC-SVM. The accuracy of the 10-fold cross-validation reached 82.0 and 86.5% in the development sets and the validation sets, respectively.

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“…The CART not only does not require a specification of the function to model covariates, but also its prediction accuracy increases with additional treatment toxicity information. 32 It seems that tree structure has a good potential to interpret nonlinear relationships and to be integrated with other NIP ML approaches for prediction accuracy improvement. Gradient boosting machine (GBM) intends to produces a prediction model by combing weak prediction decision trees, and it has been employed to predict long-term meningioma, 9 outcomes after radiosurgery for cerebral arteriovenous malformations with a high prediction performance and a less interpretability.…”

Section: Balancing Accuracy and Interpretability Of Interprmentioning

confidence: 99%

“…But they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. 54 Although the potential of DL to improve prediction accuracy may outweigh their interpretability challenges in many industries, 32,54 professionals in the field of radiation oncology are working mostly with distributed heterogeneous and complex sources of data, and there must be a possibility to make the results re-traceable on demand. 54 There has been increasing interest in radiation oncology to make DL transparent, interpretable, and explainable, and the efforts to improve its interpretability for radiation outcomes prediction are summarized in the next section.…”

Section: Balancing Accuracy and Interpretability Of Non-intmentioning

confidence: 99%

Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling

Luo

Tseng

Cui³

et al. 2019

BJR|Open

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Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.

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Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy

Cited by 29 publications

References 44 publications

Evaluating machine learning algorithms for predicting maize yield under conservation agriculture in Eastern and Southern Africa

Evaluating machine learning algorithms for predicting maize yield under conservation agriculture in Eastern and Southern Africa

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling

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