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Soft Computing

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03.04.2018 | Methodologies and Application

Diagnosis system for imbalanced multi-minority medical dataset

verfasst von: Swati Shilaskar, Ashok Ghatol

Erschienen in: Soft Computing | Ausgabe 13/2019

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Abstract

Medical datasets inherently suffer from imbalance problem. Occurrence of some of the sub-pathologies is scarce than the other. In this work, a disease diagnosis system for multiclass classification is developed. Hybrid synthetic sampling technique is used for extremely imbalanced datasets. Cluster-based self-class algorithm is proposed in this work. Compared to near miss algorithm, this exhibits equivalent performance with reduced time for sampling. The results of classification are compared across baseline approaches which do not consider clustering and synthetic sampling. A new technique based on confidence measure is proposed to evaluate test samples by OVO classifiers. This technique along with hybrid sampling suggests an improvement over the classical approaches currently used in disease diagnosis systems.

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Titel: Diagnosis system for imbalanced multi-minority medical dataset
verfasst von: Swati Shilaskar
Ashok Ghatol
Publikationsdatum: 03.04.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 13/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3133-x

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