Int. J. Data Analysis Techniques and Strategies, Vol. 12, No. 2, 2020 99 Copyright © 2020 Inderscience Enterprises Ltd. A novel integrated principal component analysis and support vector machines-based diagnostic system for detection of chronic kidney disease Aditya Khamparia* Department of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India Email: aditya.khamparia88@gmail.com *Corresponding author Babita Pandey Department of Computer Applications, Lovely Professional University, Jalandhar, Punjab, India Email: shukla_babita@yahoo.com 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. Reference to this paper should be made as follows: Khamparia, A. and Pandey, B. (2020) ‘A novel integrated principal component analysis and support vector machines-based diagnostic system for detection of chronic kidney disease’, Int. J. Data Analysis Techniques and Strategies, Vol. 12, No. 2, pp.99–113. Biographical notes: Aditya Khamparia is working as an Assistant Professor in Department of Computer Science and Engineering at the Lovely Professional University, Punjab, India. He has about five years of teaching experience and his research interests are semantic information processing and semantic web, e-learning, machine learning, soft computing and data mining.