Parameter evolution of the classifiers for disease diagnosis with offline data-driven hybrid systems

Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance b...

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Bibliographic Details
Published in:Intelligent data analysis 2020-01, Vol.24 (6), p.1365-1384
Main Authors: Nalluri, Madhu Sudana Rao, K, Kannan, Gao, Xiao-Zhi, V, Swaminathan, Roy, Diptendu Sinha
Format: Article
Language:English
Subjects:
Classification
Classifiers
Datasets
Diagnosis
Evolutionary algorithms
Genetic algorithms
Hybrid systems
Medical diagnosis
Optimization
Physicians
Statistical tests
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Summary:Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance by a multi-objective evolutionary algorithm. We have used sequential minimal optimization (SMO) classifier as the base classifier and three evolutionary algorithms namely Cat Swarm Optimization (CSO), Invasive Weed Optimization (IWO) and Eagle Search based Invasive Weed Optimization (ESIWO) to diagnose disease from datasets available. In that sense, our approach is an offline data-driven approach with 18 benchmark medical datasets, and the obtained results demonstrate the superiority of the proposed diagnoses in terms of multiple objectives such as classification Prediction accuracy, Sensitivity, and Specificity. Relevant statistical tests have been carried out to substantiate the cogence of the obtained results.
ISSN:1088-467X
1571-4128
DOI:10.3233/IDA-194687