Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
BioData Min. 2023 Apr 25;16(1):15. doi: 10.1186/s13040-023-00330-4.
ABSTRACT
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-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data.
PMID:37098549 | DOI:10.1186/s13040-023-00330-4
![Google](https://www.google.com/images/branding/googlelogo/2x/googlelogo_light_color_92x30dp.png)
![Google Keep](https://www.gstatic.com/images/branding/product/1x/keep_48dp.png)
![Share on Linkedin](https://psychiatryai.com/wp-content/uploads/2023/10/linkedin-logo-png-2048-1.png)
Estimated reading time: 4 minute(s)
Latest: Psychiatryai.com #RAISR4D Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Cool Evidence: Engaging Young People and Students in Real-World Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Real-Time Evidence Search [Psychiatry]
![](/wp-content/uploads/2024/04/pubmed.png)
AI Research
![](/wp-content/uploads/2024/05/Il5nR_nf_400x400-modified-1.png)
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
🌐 90 Days
Evidence Blueprint
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)