Principal Component Analysis of Categorized Polytomous Variable-Based Classification of Diabetes and Other Chronic Diseases

Musa Uba Muhammad, Ren Jiadong, Noman Sohail Muhammad, Munawar Hussain and Irshad Muhammad
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Musa Uba Muhammad: Department of Information sciences and Technology, Yanshan University, Qinhuangdao, Hebei 066000, China
Ren Jiadong: Department of Information sciences and Technology, Yanshan University, Qinhuangdao, Hebei 066000, China
Noman Sohail Muhammad: Department of Information sciences and Technology, Yanshan University, Qinhuangdao, Hebei 066000, China
Munawar Hussain: Department of Information sciences and Technology, Yanshan University, Qinhuangdao, Hebei 066000, China
Irshad Muhammad: Department of Information sciences and Technology, Yanshan University, Qinhuangdao, Hebei 066000, China

IJERPH, 2019, vol. 16, issue 19, 1-15

Abstract: A chronic disease diabetes mellitus is assuming pestilence proportion worldwide. Therefore prevalence is important in all aspects. Researchers have introduced various methods, but still, the improvement is a need for classification techniques. This paper considers data mining approach and principal component analysis (PCA) techniques, on a single platform to approaches on the polytomous variable-based classification of diabetes mellitus and some selected chronic diseases. The PCA result shows eigenvalues, and the total variance is explained for the principal components (PCs) solution. Total of twelve attributes was analyzed with the intention to precise the pattern of the correlation with minimum factors as possible. Usually, factors with large eigenvalues retained. The first five components have their eigenvalues large enough to be retained. Their variances are 18.9%, 14.0%, 13.6%, 10.3%, and 8.6%, respectively. That explains ~65.3% of the total variance. We further applied K-means clustering with the aid of the first two PCs. As well, correlation results between diabetes mellitus and selected diseases; it has revealed that diabetes patients are more likely to have kidney and hypertension. Therefore, the study validates the proposed polytomous method for classification techniques. Such a study is important in better assessment on low socio-economic status zone regions around the globe.

Keywords: diabetes mellitus; cardiovascular problem; data mining; classification; eigenvalues; correlation coefficient; hypertension; PCA; variance (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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