Title: A novel integrated principal component analysis and support vector machines-based diagnostic system for detection of chronic kidney disease
Authors: Aditya Khamparia; Babita Pandey
Addresses: Department of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India ' Department of Computer Applications, Lovely Professional University, Jalandhar, Punjab, India
Abstract: The alarming growth of chronic kidney disease has become a major issue in our nation. The kidney disease does not have specific target, but individuals with diseases such as obesity, cardiovascular disease and diabetes are all at increased risk. On the contrary, there is no such awareness about related kidney disease and its failure which affects individual's health. Therefore, there is need of providing advanced diagnostic system which improves health condition of individual. The intent of proposed work is to combine emerging data reduction technique, i.e., principal component analysis (PCA) and supervised classification technique support vector machine (SVM) for examination of kidney disease through which patients were being suffered from past. Variety of statistical reasoning and probabilistic features were encountered in proposed work like accuracy and recall parameters which examine the validity of dataset and obtained results. Experimental results concluded that SVM with Gaussian radial basis kernel achieved higher precision and performed better than other models in term of diagnostic accuracy rates.
Keywords: principal component analysis; PCA; support vector machine; SVM; classification; kidney disease; kernel; feature extraction.
International Journal of Data Analysis Techniques and Strategies, 2020 Vol.12 No.2, pp.99 - 113
Received: 25 May 2017
Accepted: 03 Jun 2018
Published online: 30 Mar 2020 *