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Diagnosis system for imbalanced multi-minority medical dataset

  • Swati Shilaskar
  • Ashok Ghatol
Methodologies and Application

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.

Keywords

Confidence measure Cluster Medical diagnosis system Near miss-2 Self-class Synthetic sampling 

Notes

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

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

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Govt College of EngineeringAmravatiIndia

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