A Synthetic Minority Oversampling Technique Based on Gaussian Mixture Model Filtering for Imbalanced Data Classification

Xu, Zhaozhao; Shen, Derong; Kou, Yue; Nie, Tiezheng

doi:10.1109/tnnls.2022.3197156

Cited by 22 publications

(15 citation statements)

References 51 publications

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“…The challenge in constructing predictive models for postoperative delirium, especially in the context of abdominal malignant tumors, lies in the inadequacy of traditional statistical methods when dealing with imbalanced datasets. Such methods often compromise predictive accuracy, a critical issue highlighted in previous research[ 15 , 16 ]. Addressing this, the SMOTE has been recognized as an effective solution.…”

Section: Discussionmentioning

confidence: 99%

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

Hu,

Bai,

Wang

et al. 2024

World J Gastrointest Oncol

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BACKGROUND Postoperative delirium, particularly prevalent in elderly patients after abdominal cancer surgery, presents significant challenges in clinical management. AIM To develop a synthetic minority oversampling technique (SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients. METHODS In this retrospective cohort study, we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022. The incidence of postoperative delirium was recorded for 7 d post-surgery. Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not. A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium. The SMOTE technique was applied to enhance the model by oversampling the delirium cases. The model’s predictive accuracy was then validated. RESULTS In our study involving 611 elderly patients with abdominal malignant tumors, multivariate logistic regression analysis identified significant risk factors for postoperative delirium. These included the Charlson comorbidity index, American Society of Anesthesiologists classification, history of cerebrovascular disease, surgical duration, perioperative blood transfusion, and postoperative pain score. The incidence rate of postoperative delirium in our study was 22.91%. The original predictive model (P1) exhibited an area under the receiver operating characteristic curve of 0.862. In comparison, the SMOTE-based logistic early warning model (P2), which utilized the SMOTE oversampling algorithm, showed a slightly lower but comparable area under the curve of 0.856, suggesting no significant difference in performance between the two predictive approaches. CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods, effectively addressing data imbalance.

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Section: Discussionmentioning

confidence: 99%

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

Hu,

Bai,

Wang

et al. 2024

World J Gastrointest Oncol

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“…In real-world medical studies, where the low incidence of disease makes imbalanced data more common, and ML classifiers will provide a bias towards higher predictive accuracy for most classes, ML may face challenges when encountering class-imbalanced data [ 32 ]. The Synthetic Minority Oversampling Technique (SMOTE) is based on the feature space of the samples and increases the number of minority class samples in the dataset by interpolating them to synthesize the minority class samples to achieve sample balance with the majority class [ 33 ]. The SMOTE method allows the ML model to fully learn the features of the minority class samples, improve the generalization ability and accuracy of the model, and reduce overfitting [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

Cao,

2024

Nutr Metab (Lond)

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Background Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. Methods Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005–2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. Results An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798–0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. Conclusion The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.

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“…Despite all of the above considerations, as with any other supervised intrusion detection techniques, there is a risk as-sociated with the new attack surfaces [33]. Nevertheless, this risk can be mitigated by updating the training data with new samples, which is a common approach in supervised deep learning-based solutions [34], [35].…”

Section: Efforts To Address the New Attack Surfacesmentioning

confidence: 99%

A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices

Jahangir

Lakshminarayana

Maple

et al. 2023

IEEE Internet Things J.

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The growing integration of high-wattage Internetof-Things (IoT)-enabled electrical appliances at the consumer end has created a new attack surface that an adversary can exploit to disrupt power grid operations. Specifically, dynamic load-altering attacks (D-LAAs), accomplished by an abrupt or strategic manipulation of a large number of consumer appliances in a botnet-type attack, have been recognized as major threats that can potentially destabilize power grid control loops. This paper introduces a novel approach based a multi-output network (two-dimensional convolutional neural networks classifier and reconstruction decoder) -called "2DR-CNN"-to detect and localize D-LAAs with high resolution. To achieve this, we leverage the frequency and phase angle data of the generator buses monitored by phasor measurement units (PMUs) installed in the power grid. To verify the effectiveness of the proposed method, simulations are conducted on IEEE 14-and 39-bus systems. The performance of the 2DR-CNN method is compared against several benchmark machine learning-based approaches. The results confirm that the proposed method outperforms other techniques in detection and localizing D-LAAs with high resolution in a number of practical scenarios, including PMU measurement noises and missing measurements.

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A Synthetic Minority Oversampling Technique Based on Gaussian Mixture Model Filtering for Imbalanced Data Classification

Cited by 22 publications

References 51 publications

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices

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