**1. Introduction**

Diabetes mellitus (DM) is an exponentially growing disease across the developing countries of the 21st century. Diabetes mellitus has now become a worldwide challenge and identified as the risk factor of other chronic diseases such as hyperosmolar, diabetic ketoacidosis, and hyperglycemia and, in extreme cases, death. Furthermore, diabetes also causes long-term complications, for instance, cardiovascular disease, heart stroke, kidney failure, chronic ulcers, blindness, damage to the eyes, and many more [1]. Williams wrote in his book "Williams textbook of Endocrinology" [2] that around 385 million people were a ffected with diabetes in 2013. If Diabetes mellitus is left untreated, this figure can ge<sup>t</sup> higher; it can even lead to death. Around 425 million people had diabetes in the world by the survey report of the International Diabetes Federation (IDF) in 2015 [3]. Also, the report indicates that 382 million people around the globe are a ffected by diabetes in developing countries alone and Africa has 4.9% from this ratio.

By the World Health Organization (WHO) [4], 321,100 deaths occurred in the African region due to diabetes, out of which 79% of the population was under the age of 60; this is the maximum number in any region of the world. The ratio of diabetes mellitus patients in rural and urban areas of Nigeria varies from 0.67% to 12%, and this ratio has been estimated to more than double over the past two decades [5]. According to the IDF report, the ratio of undiagnosed diabetic people in sub-Saharan Africa (SSA) is estimated at 87%, out of which 8.7% in the male and 8.9% in the female population of Nigeria. It is due to the lack of information and governmen<sup>t</sup> resources [6]. In addition, the American Diabetes Association (ADA) estimates that the prevalence was estimated in Nigeria as 20.01% in both the male and female population [7]. Compared with the world population, the Nigerian health organizations pointed out that the diabetes prevalence was 4.7% in 2010 and it was projected to be 5.8% by 2030 and even exceed 10% by 2040 [8]. However, this estimate comes from rural areas, and it is expected to be more in urban slums.

In this study, the fundamental objective was to develop a quick and accurate prediction assessment scheme by using easily observable clinical features to identify patients with a high risk of diabetes. For this purpose, the machine learning Rule classifiers (projective adaptive response theory (PART) and Decision table) were used on the Weka 3.9.2 platform for acquiring accuracy in classification assumptions. Afterward, the logistic regression (LR) was utilized on the classification results to predict and forecast patients with a high risk of diabetes. This research can be applied to diabetes mellitus patients who cannot a fford the expenses of the medical laboratory and specifically those in remote areas or villages with low socioeconomic status and excessive epidemiological risk.

Correspondingly, the remaining paper is structured as follows: Section 2 explains the material and methodology after the background description, Section 3 reviews the results, Section 4 discusses the results and limitations, and Section 5 concludes the findings.
