A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis

Fu, Yang; Wang, Kang; Sun, Lisha; Zhai, Mengjiao; Song, Jiejie; Wang, Hong

doi:10.1186/s12911-022-02075-2

Cited by 21 publications

(10 citation statements)

References 40 publications

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“…The result portrayed that the application of SMOTE-ENN increased model performance, and 90% accuracy was achieved by RFC. Yang et al [14] used a hybrid sampling method to identify missed abortion diagnoses via ensemble AI learners. The result was compared with 11 sampling algorithms, and nally, maximum e cacy was reported via RFC.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners

GhoshRoy,

Alvi,

Santosh

2024

Int. J. Patt. Recogn. Artif. Intell.

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Designing effective AI models becomes a challenge when dealing with imbalanced/skewed class distributions in datasets. Addressing this, re-sampling techniques often come into play as potential solutions. In this investigation, we delve into the male fertility dataset, exploring 14 re-sampling approaches to understand their impact on enhancing predictive model performance. The research employs conventional AI learners to gauge male fertility potential. Notably, five ensemble AI learners are studied, their performances are compared, and their results are evaluated using four measurement indices. Through comprehensive comparative analysis, we identify substantial enhancement in model effectiveness. Our findings showcase that the LightGBM model with SMOTE-ENN re-sampling stands out, achieving an efficacy of 96.66% and an F1-Score of 95.60% through 5-fold cross-validation. Interestingly, the CatBoost model, without re-sampling, exhibits strong performance, achieving an efficacy of 86.99% and an F1-Score of 93.02%. Furthermore, we benchmark our approach against state-of-the-art methods in male fertility prediction, particularly highlighting the use of re-sampling techniques like SMOTE and ESLSMOTE. Consequently, our proposed model emerges as a robust and efficient computational framework, promising accurate male fertility prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners

GhoshRoy,

Alvi,

Santosh

2024

Int. J. Patt. Recogn. Artif. Intell.

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“…Once the classification model framework was established, signatures from the fused LASSO and mRMR models were fed into the RF algorithm, and the model was trained and validated independently using 5-fold cross-validation. To maintain a sample balance of data across label categories in the training set during model training, we used the synthetic minority oversampling technique 22,23 to balance the different data label categories.…”

Section: Interpretable Hybrid Feature Radiomics Model Constructionmentioning

confidence: 99%

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules

Zhang,

Yang,

Yang

et al. 2023

J Comput Assist Tomogr

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Objectives The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. Methods In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. Results The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70–0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60–0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13–1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10–0.30; P < 0.001). Conclusion The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. Clinical relevance statement As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.

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“…It combines SMOTE and ENN approaches. Yang et al 68 proposed a hybrid‐based undersampling approach for handling overlapped and imbalanced datasets based on SMOTE and ENN. In the first phase, synthetic minority instances are generated to make the dataset balanced and in the next phase ENN approach is applied for removing overlapped majority instances.…”

Section: Literature Reviewmentioning

confidence: 99%

Entropy and improved k‐nearest neighbor search based under‐sampling (ENU) method to handle class overlap in imbalanced datasets

Kumar,

Singh,

Yadav

2023

Concurrency and Computation

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SummaryMany real‐world application datasets such as medical diagnostics, fraud detection, biological classification, risk analysis and so forth are facing class imbalance and overlapping problems. It seriously affects the learning of the classification model on these datasets because minority instances are not visible to the learner in the overlapped region and the performance of learners is biased towards the majority. Undersampling‐based methods are the most commonly used techniques to handle the above‐mentioned problems. The major problem with these methods is excessive elimination and information loss, that is, unable to retain potential informative majority instances. We propose a novel entropy and neighborhood‐based undersampling (ENU) that removed only those majority instances from the overlapped region which are having less informativeness (entropy) score than the threshold entropy. Most of such existing methods improved sensitivity scores significantly but not in many other performance contexts. ENU first computes entropy and threshold score for majority instances and, a local density‐based improved KNN search is used to identify overlapped majority instances. To tackle the problem effectively ENU defined four improved KNN‐based procedures (ENUB, ENUT, ENUC, and ENUR) for effective undersampling. ENU outperformed in sensitivity, G‐mean, and F1‐score average ranking with reduced information loss as compared to the existing state‐of‐the‐art methods.

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A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis

Cited by 21 publications

References 40 publications

Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners

Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules

Entropy and improved k‐nearest neighbor search based under‐sampling (ENU) method to handle class overlap in imbalanced datasets

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