To improve the efficiency of elderly assessments, an influence-based fast
preceding questionnaire model (FPQM) is proposed. Compared with traditional
assessments, the FPQM optimizes questionnaires by reordering their attributes.
The values of low-ranking attributes can be predicted by the values of the
high-ranking attributes. Therefore, the number of attributes can be reduced
without redesigning the questionnaires. A new function for calculating the
influence of the attributes is proposed based on probability theory. Reordering
and reducing algorithms are given based on the attributes' influences. The
model is verified through a practical application. The practice in an
elderly-care company shows that the FPQM can reduce the number of attributes by
90.56% with a prediction accuracy of 98.39%. Compared with other methods, such
as the Expert Knowledge, Rough Set and C4.5 methods, the FPQM achieves the best
performance. In addition, the FPQM can also be applied to other questionnaires.