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Open AccessArticle

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

1
Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China
2
State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(21), 4089; https://doi.org/10.3390/ijerph16214089
Received: 28 September 2019 / Revised: 20 October 2019 / Accepted: 21 October 2019 / Published: 24 October 2019
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. View Full-Text
Keywords: age; gender; diabetes mellitus; Nigeria; classification; prevalence; diagnosed; K-means; real-life data age; gender; diabetes mellitus; Nigeria; classification; prevalence; diagnosed; K-means; real-life data
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MDPI and ACS Style

Muhammad, M.U.; Jiadong, R.; Muhammad, N.S.; Nawaz, B. Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach. Int. J. Environ. Res. Public Health 2019, 16, 4089.

AMA Style

Muhammad MU, Jiadong R, Muhammad NS, Nawaz B. Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach. International Journal of Environmental Research and Public Health. 2019; 16(21):4089.

Chicago/Turabian Style

Muhammad, Musa U.; Jiadong, Ren; Muhammad, Noman S.; Nawaz, Bilal. 2019. "Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach" Int. J. Environ. Res. Public Health 16, no. 21: 4089.

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