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

Ren Jiadong,Musa Uba Muhammad,Bilal Nawaz,Noman Sohail Muhammad

doi:10.3390/ijerph16214089

Ren Jiadong, Musa Uba Muhammad + Show 2 more

Open Access

https://doi.org/10.3390/ijerph16214089

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Abstract

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.

Highlights

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
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
The population comprised of a total of 3022 patients diagnosed with different conditions who were classified and the results are presented in both tables and figures

Summary

Introduction

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]. Nigeria is a country located in the western part of Africa, and with a population of approximately million it is the seventh largest population in the world [3]. The northwestern region of Nigeria is the most densely populated area among the six geopolitical zones in the country, with an estimated population of 45 million people [5]

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