ArticleInternational journal of environmental research and public health2019
Principal Component Analysis of Categorized Polytomous Variable-Based Classification of Diabetes and Other Chronic Diseases.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Plantar Tissue Characteristics in People With Diabetes With and Without Peripheral Neuropathy: A Novel Explanatory Model for DPN Risk Assessment.Journal of diabetes · 2025Article
- Machine Learning Techniques for Antimicrobial Resistance Prediction ofComputational intelligence and neuroscience · 2023Article
- Analysis of the Statistical Comparability of the Hardness and Wear of Polymeric Materials for Orthodontic Applications.Materials (Basel, Switzerland) · 2021Article
- An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method.International journal of environmental research and public health · 2021Article
- Shared Physiologic Pathways Among Comorbidities for Adults With Cerebral Palsy.Frontiers in neurology · 2021Article
- The effect of collaborative care model training on diabetic foot ulcer patients' quality of life: a semi-experimental study.Journal of diabetes and metabolic disorders · 2020Article
- Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach.International journal of environmental research and public health · 2019Article
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5 authors.
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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.
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