Best variable identification by means of data-mining and cooperative game theory

Bruch, Stefan; Ernst, Lisa; Schulz, M; Zieglowski, Leonie; Tolba, René

doi:10.1016/j.jbi.2020.103625

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…Feature selection is a practical, data-filtering evaluation procedure ( 50 ). In feature selection strategies, a subset of features from the primary dataset is picked by evaluating the relevance of the data to show inter-group impacts ( 51 ).…”

Section: Methodsmentioning

confidence: 99%

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

et al. 2023

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BackgroundThe increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal.ObjectivesThe present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran.MethodsIn this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey.ResultsThirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis.ConclusionMachine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis.

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Section: Methodsmentioning

confidence: 99%

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

et al. 2023

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“…Statistical analysis and data evaluation were performed using the R software (v4.0.3 26 ) and the recently published algorithm for identification of the best performing variable by data-mining and cooperative game theory for evaluating study criteria (MoBPS = mining on best parameter search) 27 ). Data were grouped and summarized using the dplyr 28 package.…”

Section: Methodsmentioning

confidence: 99%

A model-specific simplification of the Mouse Grimace Scale based on the pain response of intraperitoneal CCl4 injections

Ernst

Bruch

Kopaczka

et al. 2022

Sci Rep

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Despite its long establishment and applicability in mice pain detection, the Mouse Grimace Scale still seems to be underused in acute pain detection during chronic experiments. However, broadening its applicability can identify possible refinement approaches such as cumulative severity and habituation to painful stimuli. Therefore, this study focuses on two main aspects: First, five composite MGS criteria were evaluated with two independent methods (the MoBPs algorithm and a penalized least squares regression) and ranked for their relative importance. The most important variable was used in a second analysis to specifically evaluate the context of pain after an i.p. injection (intervention) in two treatment groups (CCl4 and oil (control)) at fixed times throughout four weeks in 24 male C57BL/6 N mice. One hour before and after each intervention, video recordings were taken, and the MGS assessment was performed. In this study, the results indicate orbital tightening as the most important criterion. In this experimental setup, a highly significant difference after treatment between week 0 and 1 was found in the CCl4 group, resulting in a medium-sized effect (W = 62.5, p value < 0.0001, rCCl4 = 0.64). The oil group showed no significant difference (week 0 vs 1, W = 291.5, p value = 0.7875, rcontrol = 0.04). Therefore, the study showed that the pain caused by i.p. injections was only dependent on the applied substance, and no significant cumulation or habituation occurred due to the intervention. Further, the results indicated that the MGS system can be simplified.

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“…Statistical analysis and data evaluation were performed using the R software (v4.0.3 28 ) and the recently published algorithm for identi cation of the best performing variable by data-mining and cooperative game theory for evaluating study criteria (MoBPS = mining on best parameter search) 29 ). Data were grouped and summarized using the dplyr 30 package.…”

Section: Data Science and Analysismentioning

confidence: 99%

Closing an Eye for the Reliability of Orbital Tightening in Pain Detection: A Model-Specific Simplification of the Mouse Grimace Scale

Ernst

Bruch

Kopaczka

et al. 2021

Preprint

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Despite its long establishment and its applicability in pain detection in mice, the Mouse Grimace Scale still seems to be underused in terms of acute pain detection during chronic experiments. However, a broadening of its applicability can identify possible refinement approaches such as cumulative severity and habituation to painful stimuli. Therefore, this study focuses on two main aspects: First, five composite MGS criteria were evaluated with two independent methods (the MoBPs algorithm and a penalized least squares regression) and ranked for their relative importance. The most important variable was used in a second analysis to specifically evaluate the context of pain after an i.p. injection (intervention) in two treatment groups (CCl4 and oil (control)) at fixed times throughout four weeks in 24 male C57BL/6N mice. One hour before and after each intervention, video recordings were taken and the MGS assessment was performed. In this study, the results indicate orbital tightening as the most important criterion. In this experimental setup, a highly significant difference after treatment between week 0 and 1 was found in the CCl4 group, resulting in a medium-sized effect (W = 62.5, p-value <0.0001, rCCl4= 0.64). The oil group showed no significant difference (week 0 vs 1, W = 291.5, p-value = 0.7875, rcontrol= 0.04). Therefore, the study showed that the pain caused by i.p. injections was only dependent on the applied substance and no significant cumulation or habituation occurred due to the intervention. Further, the results indicated that the MGS system can be simplified.

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Best variable identification by means of data-mining and cooperative game theory

Cited by 8 publications

References 19 publications

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

A model-specific simplification of the Mouse Grimace Scale based on the pain response of intraperitoneal CCl4 injections

Closing an Eye for the Reliability of Orbital Tightening in Pain Detection: A Model-Specific Simplification of the Mouse Grimace Scale

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