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

Musa Uba Muhammad,Ren Jiadong,Irshad Muhammad,Noman Sohail Muhammad,Munawar Hussain

doi:10.3390/ijerph16193593

Musa Uba Muhammad, Ren Jiadong + Show 3 more

Open Access

https://doi.org/10.3390/ijerph16193593

Copy DOI

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.

Highlights

Diagnosis of chronic diseases is essential in the healthcare field as these diseases persist for a long time
We further applied K-means clustering with the aid of the first two principal components (PCs)
Correlation results between diabetes mellitus and selected diseases; it has revealed that diabetes patients are more likely to have kidney and hypertension

Summary

Introduction

Diagnosis of chronic diseases is essential in the healthcare field as these diseases persist for a long time. The major chronic diseases include diabetes, cardiovascular disease, kidney problem, cancer, and stoke. Classification of the disease helps in taking precautionary actions, and effective treatment at an initial stage found to be helpful for patients [1]. Principal component analysis (PCA) a trendy method for data reduction, found to be a useful step in classification [2]. PCA and machine learning methods were successfully applied in medical domains, for example, in the diagnosis of diabetes aspects, therapy, prognostics of recurrence of breast cancer, localization of a primary tumor, and diagnosis of thyroid diseases. Polytomization of variables may occur in situation categorical variables are of multiple outcomes, for more subjective assessment and evaluation, as the factors scores obtained from binary variables are linearly related [1,2,3]

Methods

Findings

Discussion

Conclusion