Diagnosis system for imbalanced multi-minority medical dataset
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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.
KeywordsConfidence measure Cluster Medical diagnosis system Near miss-2 Self-class Synthetic sampling
Compliance with ethical standards
Conflicts of interest
The authors declare that they have no conflicts of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Human and animals rights statement
This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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