4. Discussion
FHL
REGRESSION
The aim of this study was to investigate the use of machine learning to develop a short test of FHL in adults (the DS-TOFHLA) that can be used in the development of short versions of the TOFHLA in various languages, including the original version of the American TOFHLA in English. In addition to investigating the machine learning approach, this study also addressed the problem that, to the authors’ knowledge, there are no efficient, suitable, and objective screening instruments for assessing HL in a clinical setting in Denmark and other non-English speaking countries.A review has shown that most studies using the S-TOFHLA chose to omit the numeracy items to simplify the test and make it usable in a clinical setting [For comparison, an assessment of the performance of the S-TOFHLA was performed using Danish mirror versions of the short American versions. Both the S-TOFHLA and the Prose S-TOFHLA (without numeracy items) was assessed, the latter being the most widely used [The DS-TOFHLA was developed solely using an algorithm-based selection of variables and MLRs. A major strength of this method was that the design principles were founded on objective algorithm-based decisions, and the MLR used for development selected the items from the D-TOFHLA that led to the most accurate predictions of the level of FHL. The reading comprehension items in both the D-TOFHLA and the American TOFHLA are ordered by increasing difficulty in readability level and thus, it is reasonable to assume that the more difficult items are most accurate in predicting the functional level of HL. In this regard, it should be noted that only 4 of 20 items selected for the DS-TOFHLA by the algorithm herein were from the first and easiest part of the reading comprehension items. The 36 items assessing reading comprehension in the S-TOFHLA is primarily from the first and middle part (lowest difficulty) and none from the latter and most difficult part. In line with this, the regression model presents quite different weights to the selected items for the DS-TOFHLA, e.g., C42 = 0 and C43 = 6. In comparison, the development of the American S-TOFHLA seems based on a subjective decision to include the first 36 items, without explicitly considering if these items contribute to the most accurate prediction of the FHL or considering the ascending difficulty in readability.The accuracy of the prediction of the three levels was ranged from 80%-92%; the middle level had the lowest accuracy, which can be explained by the fact that this level is defined by a relatively narrow range of 60–74 points. The prediction of the lowest and highest levels was a one-sided classification, whereas the prediction of the middle layer was two-sided. It should be noted that the prediction of ‘low FHL’ (i.e., inadequate or marginal vs. adequate scores), as defined by Parker et al [During the development of a novel questionnaire, it is customary to adhere to established guidelines for scale development and validation. The DS-TOFHLA was based on a predictive MLR and should not be regarded as a de novo questionnaire, and it, therefore, should not go through the same rigorous evaluation. Instead, the focus should be on developing a short questionnaire with the best possible predictions of the validated full-length questionnaire. Likewise, it makes sense to develop the prediction model in the DS-TOFHLA based on the same data that was used to develop and validate the full-length Danish TOFHLA [