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

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.

Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach

“…The recent advances in biotechnology and health sciences have led to a significant production of data such as clinical information generated from massive electronic health records. Machine learning methods have been successfully applied several times in medical domains, for example, in the diagnosis of diabetes aspects and epidemiological studies [1]. Epidemiological studies are based on a data mining approach, which constitutes machine learning 60%, statistics 35% and probability 5%, according to various studies [2].…”

“…Insulin is a hormone that controls blood sugar. Hyperglycemia or higher blood sugar is a common effect of uncontrolled diabetes and, over time, this leads to severe damage to systems throughout the entire human body [1]. The disease assumes pestilence proportion worldwide, by affecting both developed and underdeveloped countries [5].…”

“…The disease assumes pestilence proportion worldwide, by affecting both developed and underdeveloped countries [5]. The recent prevalence figure published by the International Diabetes Federation is 425 million people living with diabetes mellitus comprehensively, with virtually 50% as non-diagnosed cases [1].…”

Analysis of the Statistical Comparability of the Hardness and Wear of Polymeric Materials for Orthodontic Applications

“…According to the literature [ 52 , 58 , 59 ], one should choose the smallest number of principal components for which the sum of their variances is a certain fraction of the variance of all the variables under reduction. The lower bound that the sum must exceed is, according to various sources, 75, 80, or even 90% [ 52 , 58 , 60 ].…”

An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method

“…BMI was measured as body weight, separated by height squared into meters, and BMI (≥25) was defined as overweight. These five attributes were taken into account from our previously reported prevalence research [ 23 , 27 , 28 , 29 , 35 , 36 ]. Furthermore, the characteristics of smoking (yes or no) were applied to this study because smoking has been a significant factor in human depression or distress in clinical trials and psychiatric expertise [ 37 ].…”