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Emmanuel N. Nartey, Ph.D. is a Research Scientist for the Center for Biostatistics and Health Data Science. He is a recent graduate from the Department of Statistics, Actuarial, and Data Sciences at Central Michigan University and beginning his career at the CBHDS. His research interests include supervised and unsupervised machine learning, statistical computing, complex sample data analysis, generalized linear models, and survival analysis. His dissertation studied internal indices for validating clustering solutions and using those indices to guide feature selection for classification. Emmanuel has experience working with national health surveys and electronic health records and has contributed to research studies in different health areas including eating disorders, HPV vaccination, and Medicare payments.

Nartey, E. N., Lee, C., & Famoye, F. A comparative review of some internal validation techniques for determining the number of clusters in numeric data. (manuscript in preparation)

Nartey, E. N., Lee, C., & Famoye, F. Numeric feature selection via the Average Silhouette Score towards optimal classification in machine learning. (manuscript in preparation)

Ameh, G., Nartey, E., Inungu, J., Shayestah, J. & Uchechukwu, O. (2023). Racial Disparities in Oral Health; Analysis of 2020 Behavioral Risk Factor Surveillance System. Acta Scientific Dental Sciences, Vol. 7, pp. 29-39. DOI: 10.31080/ASDS.2023.07.1551