SMOTE-CD: SMOTE for compositional data

Nguyen, Teo; Mengersen, Kerrie; Sous, Damien; Liquet, Benoît

doi:10.1371/journal.pone.0287705

Cited by 10 publications

(2 citation statements)

References 42 publications

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“…The SMOTE was proposed by Chawla [ 23 ] in 2002, which synthesized better classifier performance by using the interpolation method combining the over-sampling of the minority class and the under-sampling of the majority class. N was the number of the minority classified in the training set, the process was as follows:…”

Section: Methodsmentioning

confidence: 99%

Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning

Tang,

Liu,

et al. 2024

BMC Pediatr

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Objective Kawasaki syndrome (KS) is an acute vasculitis that affects children < 5 years of age and leads to coronary artery lesions (CAL) in about 20-25% of untreated cases. Machine learning (ML) is a branch of artificial intelligence (AI) that integrates complex data sets on a large scale and uses huge data to predict future events. The purpose of the present study was to use ML to present the model for early risk assessment of CAL in children with KS by different algorithms. Methods A total of 158 children were enrolled from Women and Children’s Hospital, Qingdao University, and divided into 70–30% as the training sets and the test sets for modeling and validation studies. There are several classifiers are constructed for models including the random forest (RF), the logistic regression (LR), and the eXtreme Gradient Boosting (XGBoost). Data preprocessing is analyzed before applying the classifiers to modeling. To avoid the problem of overfitting, the 5-fold cross validation method was used throughout all the data. Results The area under the curve (AUC) of the RF model was 0.925 according to the validation of the test set. The average accuracy was 0.930 (95% CI, 0.905 to 0.956). The AUC of the LG model was 0.888 and the average accuracy was 0.893 (95% CI, 0,837 to 0.950). The AUC of the XGBoost model was 0.879 and the average accuracy was 0.935 (95% CI, 0.891 to 0.980). Conclusion The RF algorithm was used in the present study to construct a prediction model for CAL effectively, with an accuracy of 0.930 and AUC of 0.925. The novel model established by ML may help guide clinicians in the initial decision to make a more aggressive initial anti-inflammatory therapy. Due to the limitations of external validation and regional population characteristics, additional research is required to initiate a further application in the clinic.

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Section: Methodsmentioning

confidence: 99%

Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning

Tang,

Liu,

et al. 2024

BMC Pediatr

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“…The normalization process employed in this study was "MinMaxScaler," which linearly transformed the original data to ensure that the resulting values fell within the range of [0, 1]. Additionally, data balancing was performed using the "SMOTE" technique, which equalized the proportion of minority and majority classes to a ratio of 1:1 (Nguyen et al, 2023).…”

Section: Data Preprocessingmentioning

confidence: 99%

The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model

Chen,

Zhao,

Yang

et al. 2024

Preprint

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Background The relationship between the impact of anti-involution training on critical thinking and its propensity indicators among young healthcare professionals in dental outpatient clinics remains to be determined. Therefore, this study aimed to investigate these associations and develop an interpretable machine learning (ML) model to assess their predictive value in enhancing critical thinking through anti-involution training. Methods A cross-sectional survey encompassing 114 participants was conducted. Spearman correlation analysis was utilized to evaluate the association between propensity indicators and the enhancement of critical thinking through anti-involution training. Subsequently, the data underwent normalization utilizing the “MinMaxScaler” technique, while balancing was achieved by applying the synthetic minority oversampling technique (SMOTE). Following this, predictors were identified using the most minor absolute shrinkage and selection operator (LASSO) regression. Next, diverse machine learning algorithms constructed an individual prediction model to enhance critical thinking through anti-involution training. The prediction model's performance was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The Shapley additive interpretation (SHAP) method was utilized to interpret the ML model. Results Truth-seeking, analytical thinking, and inquisitiveness were identified as predictive factors for enhancing critical thinking. A Random Forest algorithm-based model incorporating these variables yielded favorable results: AUC = 0.889 (95% CI: 0.839–0.937), accuracy = 0.850, sensitivity = 0.855, specificity = 0.933. Conclusion The inclinations toward truth-seeking, analytical thinking, and inquisitiveness significantly correlate with the effectiveness of anti-involution training in enhancing critical thinking. Our simplified ML-based predictive model allows for preliminary forecasting, enabling early intervention and guidance for learners facing difficulties in improving critical thinking.

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Enhancing Malware Detection Through Machine Learning Using XAI with SHAP Framework

Basheer,

Pranggono,

Islam

et al. 2024

IFIP Advances in Information and Communication Technology

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SMOTE-CD: SMOTE for compositional data

Cited by 10 publications

References 42 publications

Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning

Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning

The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model

Enhancing Malware Detection Through Machine Learning Using XAI with SHAP Framework

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