A novel integrated principal component analysis and support vector machines-based diagnostic system for detection of chronic kidney disease
by Aditya Khamparia; Babita Pandey
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 12, No. 2, 2020

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.

Online publication date: Thu, 16-Apr-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com