About: Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets
Creator	Elyan, Eyad Vuttipittayamongkol, Pattaramon
Source	PMC
abstract	Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49186-4_30
has license	no-cc
sha1sum (hex)	86019d740fa972e67cbe33f723cba757cb43f5c1
schema:url	https://doi.org/10.1007/978-3-030-49186-4_30
resource representing a document's title	Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets
has PubMed Central identifier	PMC7256568
schema:publication	Artificial Intelligence Applications and Innovations
resource representing a document's body	covid:86019d740fa972e67cbe33f723cba757cb43f5c1#body_text
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