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The results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to verify fall-risk categorization; and (b) a logistic regression with 100 stepwise iterations is used to divide features into training (80%) and testing sets (20%). The features of (a) and (b) are compared, measuring the similarity of each approach’s decisive features to those of the clinical-test results. In (a), the most significant features are the Y and Z axes, regardless of the segmentation, whereas sTUG outperforms TUG in (b). Comparing the results of (a) and (b) based on the overall TUG test, the <i>Z</i> axis multiscale entropy (MSE) features show significance regardless of the approach: expert opinion or logistic prediction. Among various clinical test combinations, the only commonalities between (a) and (b) are the Y-axis MSE features when walking. Thus, machine learning should be based on both expert domain knowledge and a preliminary analysis with objective screening. Finally, the clinical test results are compared with the inertial sensor results, prompting the proposal for multi-oriented data analysis to objectively verify the sensor results.