A novel integrated principal component analysis and support vector machines-based diagnostic system for detection of chronic kidney disease

Aditya Khamparia and Babita Pandey

International Journal of Data Analysis Techniques and Strategies, 2020, vol. 12, issue 2, 99-113

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. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=106641 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:12:y:2020:i:2:p:99-113

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().