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

An accurate classification for diabetes mellitus (DBM) allows for the adequate treatment and handling of its menace, particularly in developing countries like Nigeria. This study proposes data mining techniques for the classification and identification of the prevalence of diagnosed diabetes cases, stratified by age, gender, diabetic conditions and residential area in the northwestern states of Nigeria, based on the real-life data derived from government-owned hospitals in the region. A K-mean assessment was used to cluster the instances, after 12 iterations the instances classified out of 3022: 2662 (88.09%) non-insulin dependent (NID), 176 (5.82%) insulin-dependent (IND) and 184 (6.09%) gestational diabetes (GTD). The total number of diagnosed diabetes cases was 3022: 1380 males (45.66%) and 1642 females (54.33%). The higher prevalence was found to be in females compared to males, and in cities and towns, rather than in villages (36.5%, 34.2%, and 29.3%, respectively). The highest prevalence among the age groups was in the age group 50-69 years, which constituted 43.9% of the total diagnosed cases. Furthermore, the NID condition had the highest prevalence of cases (88.09%). These were the first findings of the stratified prevalence in the region, and the figures have been of utmost significance to the healthcare authorities, policymakers, clinicians, and non-governmental organizations for the proper planning and management of diabetes mellitus.

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

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.