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Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.

Publication Type : Journal Article

Publisher : Journal of Healthcare Engineering

Source : Algorithms, Diagnosis, Computer-Assisted, Humans, sensitivity and specificity, Support Vector Machine

Url : https://doi.org/10.1155/2017/5907264

Keywords : Algorithms, Diagnosis, Computer-Assisted, Humans, sensitivity and specificity, Support Vector Machine

Campus : Coimbatore

School : School of Engineering

Center : Humanitarian Technology (HuT) Labs

Department : Computer Science

Year : 2017

Abstract : With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

Cite this Research Publication : Madhusudana Rao Nalluri, Kannan K., Manisha M, and Diptendu Sinha Roy, “Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.”, Journal of Healthcare Engineering, vol. 2017, p. 5907264, 2017.

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