Classification of Cancer Drug Compounds for Radiation Protection Optimization Using CART

Kuswanto, Heri; Mubarok, Rizky

doi:10.1016/j.procs.2019.11.145

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…At each node, CART aims to maximise the information gain which is the difference between the original information and the information obtained after the partitioning the node [37] and it is also known as the goodness of split [38]. At each step, the information gain is computed using Equation 5 [37]:…”

Section: Cartmentioning

confidence: 99%

Modelling and Predicting Learners Numeracy Test Results using Some Regression and Machine Learning Classifiers

2023

J. Stat. Appl. Pro.

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The prediction of early childhood numeracy skills development is often studied by determining the learner's performance in a numeracy test. It is an important study area since numeracy impacts on the learner's mathematical and statistical abilities later in life. Despite having pros and cons over each other, classification algorithms are often applied in the prediction of early childhood numeracy skills development without justifying the choice of a certain algorithm over others. In this paper, the bi-directional stepwise logistic regression model (SLRM), hierarchical logistic regression model (HLRM), classification and regression tree (CART) and Naïve Bayes (NB) algorithm were compared in terms of their ability to accurately classify some learners as having passed or having failed a numeracy test. The intention was to determine the most accurate algorithm from the four relative to predicting the learner's performance in a numeracy test. The algorithms were compared using the true positive rate, true negative rate, specificity, sensitivity, classification error, classification accuracy and the area under the receiver operating characteristic (AUROC) curve. The results showed that the HLRM which has been applied by several previous studies on the prediction of numeracy test competence is the best classifier followed by SLRM, CART then NB. The study also confirmed some important predictors of the learner's performance in a numeracy test some of which were also identified by some previous studies on early childhood numeracy development. Some gaps and recommendations for future research pertaining to the classification algorithms as well as implications for practice were also highlighted. We have made the HLRM scoring algorithm generated from SPSS available as a supplementary material and can be used to classify a set of new learners to either the pass or fail group.

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Section: Cartmentioning

confidence: 99%

Modelling and Predicting Learners Numeracy Test Results using Some Regression and Machine Learning Classifiers

2023

J. Stat. Appl. Pro.

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“…The decision tree tool used for mesosystems typology is disclosed in the works of Rutkowski et al [31], Kuswanto and Mubarok [32], Xu et al [33], Breiman et al [34], Apté and Weiss [35], who consistently describe observations segmentation method, based on mathematical functions, in particular, on the calculation of the Gini index. A common modelling tool is the CART algorithm, which is primary in our study [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…The regression construction and classification trees are based on the CART algorithm, applied for classification tree construction, based on the Gini index [31][32][33][34] and the data array t that is determined by the Formula (5):…”

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confidence: 99%

Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction

et al. 2021

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Globally, assessing sustainable development methodology is kept in sustainable society index (SSI) format, but at the level of meso- and microsystems it remains undeveloped. The aim of the study is to typologize innovative mesosystems in Russian industry in the context of sustainable development based on the CART algorithm and to develop an algorithm for identifying priority areas of sustainable development. The research methods applied included formalization, a systematic approach, and the CART algorithm (calculation of the Gini index, training sample segmentation, the use of a recursive function and regression assessment). As a result of the study, the algorithm for the differentiated identification of innovative mesosystems sustainable development priority directions in industry based on the unique author’s methodology (ISDI) is proposed. The predominance of mesosystems with weak level of sustainable development requiring state support in favor of such mesosystems restructure is revealed. The novelty of the research lies in the development of new science-based solutions to ensure an accelerated transition of industry to the path of sustainable development. The difference of the author’s approach from the provisions known in science is the inclusion of environmental innovations in the mechanism for managing the sustainable development of innovative mesosystems and subsequent accounting in the process of mathematical processing of an array of data, which determines the uniqueness of the constructed decision trees.

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Diabetes Prediction by Machine Learning Algorithms and Risks Factors

Fakir

2023

Lecture Notes in Business Information Processing

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Classification of Cancer Drug Compounds for Radiation Protection Optimization Using CART

Cited by 5 publications

References 17 publications

Modelling and Predicting Learners Numeracy Test Results using Some Regression and Machine Learning Classifiers

Modelling and Predicting Learners Numeracy Test Results using Some Regression and Machine Learning Classifiers

Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction

Diabetes Prediction by Machine Learning Algorithms and Risks Factors

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