ArticleFrontiers in physiology2023
Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data.
Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- A hybrid XGBoost-SVM ensemble framework for robust cyber-attack detection in the internet of medical things (IoMT).Scientific reports · 2026Article
- Optimizing feature selection in cancer microarray data using a heap-driven evolutionary framework for high-dimensional spaces.Scientific reports · 2026Article
- Federated Learning in Healthcare Ethics: A Systematic Review of Privacy-Preserving and Equitable Medical AI.Healthcare (Basel, Switzerland) · 2026Review
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- An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
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Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields. Despite the importance of outlier detection, many existing methods are vulnerable to the distribution of outliers and require prior knowledge, such as the outlier proportion. To address this problem to some extent, this article proposes an adaptive mini-minimum spanning tree-based outlier detection (MMOD) method, which utilizes a novel distance measure by scaling the Euclidean distance. For datasets containing different densities and taking on different shapes, our method can identify outliers without prior knowledge of outlier percentages. The results on both real-world medical data corpora and intuitive synthetic datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art methods.
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