Fuzzy classifcation of imbalanced data sets for medical diagnosis

Ganji, Mostafa Fathi; Abadeh, Mohammad Saniee; Hedayati, Mehdi; Bakhtiari, Nuredine

doi:10.1109/icbme.2010.5705027

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…PROPOSED METHODOLOGY Numerous diseases constitute issues that are of problem to health authorities, physicians, as a result of problems inside their early analysis and associated mortality rates [18]. These confusing early symptoms often result in misdiagnosis, insufficient assessments, poor decision management in India.…”

Section: Related Workmentioning

confidence: 99%

Medical diagnosis system using fuzzy logic toolbox

Dagar

Jatain²,

Gaur

2015

International Conference on Computing, Communication &Amp; Automation

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Medical diagnosis is basically a pattern classification phenomena: based on some input provided by a patient, an expert gives a conclusion on the basis of its knowledge, which is normally stored in a binary form, and finally the result is calculated i.e. either the patient suffering from a certain disease or not. There are a number of properties in fuzzy set theory has a number of facilities that make it suitable for medical diagnosis. The results and findings from the study had shown that the technique of fuzzy logic can contribute a reliable result in order to notify the disease. Human subject has been used to test the system. In this paper we have proposed a system for medical diagnosis which is a generated using Fuzzy Logic Toolbox in MATLAB. Specifically, it focuses on medical diagnosis.

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Section: Related Workmentioning

confidence: 99%

Medical diagnosis system using fuzzy logic toolbox

Dagar

Jatain²,

Gaur

2015

International Conference on Computing, Communication &Amp; Automation

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Synergistic Combination of Machine Learning and Evolutionary and Heuristic Algorithms for Handling Imbalance in Biological and Biomedical Datasets

Modak,

Pandya,

Siarry

et al. 2024

Computational Intelligence Methods and Applications

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Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

Iram

Jumeily

Fergus

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-The optimization and evaluation of a pattern recognition system requires different problems like multi-class and imbalanced datasets be addressed. This paper presents the classification of multi-class datasets which present more challenges when compare to binary class datasets in machine learning. Furthermore, it argues that the performance evaluation of a classification model for multi-class imbalanced datasets in terms of simple "accuracy rate" can possibly provide misleading results. Other parameters such as failure avoidance, true identification of positive and negative instances of a class and class discrimination are also very important. We, in this paper, hypothesize that "misclassification of true positive patterns should not necessarily be categorized as false negative while evaluating a classifier for multi-class datasets", a common practice that has been observed in the existing literature. In order to address these hidden challenges for the generalization of a particular classifier, several evaluation metrics are compared for a multi-class dataset with four classes; three of them belong to different neurodegenerative diseases and one to control subjects. Three classifiers, linear discriminant, quadratic discriminant and Parzen are selected to demonstrate the results with examples.

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Fuzzy classifcation of imbalanced data sets for medical diagnosis

Cited by 7 publications

References 17 publications

Medical diagnosis system using fuzzy logic toolbox

Medical diagnosis system using fuzzy logic toolbox

Synergistic Combination of Machine Learning and Evolutionary and Heuristic Algorithms for Handling Imbalance in Biological and Biomedical Datasets

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

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