A genetic algorithm to find optimal reading test word subsets for estimating full-scale IQ

Abstract

In clinical neuropsychology the cognitive abilities of neurological patients are commonly estimated using well-established paper-based tests. Typically, scores on some tests remain relatively well preserved, whilst others exhibit a significant and disproportionate decline. Scores on those tests that measure preserved cognitive functions (so-called ‘hold’ tests) may be used to estimate premorbid abilities, including scores in non-hold tests that would have been expected prior to the onset of cognitive impairment. Many hold tests entail word reading, with each word being graded as correctly or incorrectly pronounced. Inevitably, such tests are likely to contain words that provide little or no diagnostic power (i.e., can be eliminated without negatively affecting prediction accuracy). In this paper, a genetic algorithm is developed and demonstrated, using n = 92 neurologically healthy participants, to identify optimal word subsets from the National Adult Reading Test that minimize the mean error in predicting the most widely used clinical measure of IQ and cognitive ability, the Wechsler Adult Intelligence Scale Fourth Edition IQ. In addition to requiring only 17–20 of the original 50 words (suggesting that this test could be revised to be up to 66% shorter) and minimizing mean prediction error, the algorithm increases the proportion of the variance in the predicted variable explained in comparison to using all words (from r2 = 0.46 to r2 = 0.61). In a clinical setting this would improve estimates of premorbid cognitive function and, if an abbreviated revision to this test were to be adopted, reduce the arduousness of the test for patients. The proposed method is evaluated with jackknifing and leave one out cross validation. The general approach may be used to optimize the relationship between any two psychological tests by finding the question subset in one test that minimizes the prediction error in a second test by training the genetic algorithm using data collected from participants upon whom both tests have been administered. This approach may also be used to develop new predictive tests, since it provides a method to identify an optimal subset of a set of candidate questions (for which empirical data have been collected) that maximizes prediction accuracy and the proportion of variance in the predicted variable that can be explained.

Occupational Patterns of Structural Brain Health: Independent Contributions Beyond Education, Gender, Intelligence, and Age

“…Table 1 provides sample information. IQ was assessed with the National Adult Reading Test (Nelson, 1982;Blair and Spreen, 1989).…”

The “Dutch Reading Test for Adults” has Been Used for 29 Years to Estimate the Premorbid Performance Level, does it Still Meet the Expectations?

“…Indeed, recent studies with the English version of the task still show that the principle still holds true today ( Bright et al, 2018 ). Updating the NLV at the item level can thus be a promising endeavor and will be of great value for the clinical practice and the neuropsychological researchers who are now lacking a fast and easy tool to estimate premorbid functioning (see van der Linde & Bright, 2018, for an algorithm to find the optimal set of words ).…”

Estimation of premorbid intelligence and executive cognitive functions with lexical reading tasks.

“…Although in this research we focused on the median score from three different assessments, further studies could explore whether the mean or median is the best estimators of central tendency in clinical practice. Furthermore, machine learning methods, where the best relative weights of multiple tests, or even the best items from within tests, have recently been applied to neuropsychology to improve prediction accuracy (Johnson et al, 2014; van der Linde & Bright, 2018). Such computational methods could potentially improve the accuracy of the methods described here.…”