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Data stratification is the process of partitioning the data into distinct and nonoverlapping groups since the
study population consists of subpopulations that are of particular interest. In clinical data, once the data is
stratified into sub populations based on a significant stratifying factor, different risk factors can be
determined from each subpopulation. In this paper, the Fisher’s Exact Test is used to determine the
significant stratifying factors. The experiments are conducted on a simulated study and the Medical,
Epidemiological and Social Aspects of Aging (MESA) data constructed for prediction of urinary
incontinence. Results show that, smoking is the most significant stratifying factor of MESA data, showing
that the smokers and nonsmokers indicates different risk factors towards urinary incontinence and should
be treated differently.
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