A Novel Hybrid Approach for Chronic Disease Classification

A Novel Hybrid Approach for Chronic Disease Classification

Divya Jain (Computer Science and Engineering, The NorthCap University, Gurugram, India) and Vijendra Singh (Computer Science and Engineering, The NorthCap University, Gurugram, India)
DOI: 10.4018/IJHISI.2020010101

Abstract

A two-phase diagnostic framework based on hybrid classification for the diagnosis of chronic disease is proposed. In the first phase, feature selection via ReliefF method and feature extraction via PCA method are incorporated. In the second phase, efficient optimization of SVM parameters via grid search method is performed. The proposed hybrid classification approach is then tested with seven popular chronic disease datasets using a cross-validation method. Experiments are then conducted to evaluate the presented classification method vis-à-vis four other existing classifiers that are applied on the same chronic disease datasets. Results show that the presented approach reduces approximately 40% of the extraneous and surplus features with substantial reduction in the execution time for mining all datasets, achieving the highest classification accuracy of 98.5%. It is concluded that with the presented approach, excellent classification accuracy is achieved for each chronic disease dataset while irrelevant and redundant features may be eliminated, thereby substantially reducing the diagnostic complexity and resulting computational time.
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2. Background On Existing Classifiers

Over the years, numerous machine-learning methods have been applied successfully for effective disease diagnosis (Azzawi et al., 2016; Dora et al., 2017). Owing to its many excellent features and remarkable generalization performance (Cortes & Vapnik, 1995; Critianini & Shawe-Taylor, 2005), support vector machine (SVM) classifier is a widely used learning method in huge demand today. SVM is based on finding the maximum-margin hyper-plane for the separation of two classes as wide as possible. It has gained popularity in a wide range of biological applications (Noble, 2006). Also, with the use of kernel functions, SVM works efficiently with linear as well as non-linear datasets.

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