**5. Conclusions**

This study implements the machine learning rule classifiers (PART and Decision table) on a data mining platform to identify possible diabetes and pre-diabetes in the initial clinical screening of a patient through logistic regression forecast assessment analysis. Two hundred and eighty-one diabetes mellitus patients have been analyzed with 10 easily available non-invasive medical features collected from four main hospitals located in northwestern Nigeria. The classification assessment accuracy was 98.75% and it was achieved through a set of 23-decision screening rules that can successfully influence accurate initial clinical screening of diabetes mellitus and pre-diabetes patients.

Additionally, the obtained Rules classified the most considerable risks and sugges<sup>t</sup> that diabetes prevention and education programs can be applied in targeted community interventions. The study helps in the initial diagnosis of diabetes and reduces healthcare organization problems. Therefore, such a study is found extremely significant for the states and regions with extreme epidemic risk ratios and low socioeconomic status across the globe.

**Author Contributions:** Writing—Original draft preparation, methodology, software, and formal analysis have been done by M.N.S.; conceptualization, validation, data curation, and visualization, has done by M.N.S., M.U.M.; supervision, resources, project administration, and funding acquisition has done by R.J.; investigation has done by M.N.S., S.T.C., J.A.; finally writing—review and editing has carefully done by M.N.S., S.T.C., J.A., A.J.V.

**Funding:** This research work has been supported by NSFC Natural Science Foundation of Hebei province under gran<sup>t</sup> of No. 61572420, No. 61472341, and No. 61772449.

**Acknowledgments:** We express our appreciation to "Yanshan University, Qinhuangdao, China" for accompanying us in this research.

**Conflicts of Interest:** The authors declare no conflict of interest.
