Article

A Self-inspected Adaptive SMOTE Algorithm (SASMOTE) for Highly Imbalanced Data Classification in Healthcare

2022-05-20

Abstract excerpt

In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-qual...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b88ce0fb-8923-5cd3-9e0b-c9aa0314bd1e
DOI: 10.21203/rs.3.rs-1647776/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Self-inspected Adaptive SMOTE Algorithm (SASMOTE) for Highly Imbalanced Data Classification in HealthcareDOI 10.21203/rs.3.rs-1647776/v1Read

An Artificial Immune Network Combined with Improved SMOTE Oversampling Technique-Incorporated Robust Feature Selection Method for Imbalanced Medical DataSemantic proximity Read

Improving classification on imbalanced genomic data via KDE–based synthetic samplingSemantic proximity Read

An Empirical study on the class imbalance handling techniques for different diseasesSemantic proximity Read

RNN-Fuzzy-c-means-smote and fuzzy classifier to early predict hidden diseaseSemantic proximity Read

Cervical Cancer Prediction based on Imbalanced Data using Machine Learning AlgorithmsSemantic proximity Read

The Use of Class Imbalanced Learning Methods on ULSAM Data to Predict the Case-Control Status in Genome-Wide Association StudiesSemantic proximity Read

Optimizing Machine Learning for Diabetes Detection: Addressing Class Imbalance with SMOTE and Random Forest Ensemble LearningSemantic proximity Read

Genetic Algorithm Based Oversampling Approach to Prune the Class Imbalance issue in Software Defect PredictionSemantic proximity Read

ICU Mortality and LOS Prediction Models Using Machine Learning Based on Both Real and Synthetic DataSemantic proximity Read

Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unitSemantic proximity Read

Impact of Class Imbalance on Binary Outcome Predictions: A Comparative Study of Thresholding Strategies with LASSOSemantic proximity Read

Improvement in Detecting the Fate of Covid-19 Patients and Rule-based Analysis to Discover the Most Important Rules Governing their FateSemantic proximity Read