Clinical Manifestations.

Background: Over 13,000 individuals from both domestic and African sites will be collected for the READD-ADSP study. Adjudicating this number of individuals is challenging, so we evaluated knowledge-based decision tree algorithms to predict clinical diagnoses using nationally representative norms and standard cut-offs. Additional models were constructed using culturally adjusted cut-offs, domain average cut-offs, and exclusion of the Trail Making Test (TMT) which performed poorly. Our aim was to evaluate the accuracy of these decision tree models in the READD-ADSP dataset.

Method: Clinically adjudicated diagnoses by at least two neuropsychologists and/or neurologists were available for 150 individuals from the READD-ADSP sample (44%-Unaffected, 36%-MCI, 9.3%-AD, 2.7%-Dementia not AD, 8%-Cognitively Impaired- Vascular; 19% Hispanic, 81% African-American, 76%-Female, Age: 73.0 years, Education: 13.7 years). Eight models were used to classify our sample into the diagnostic categories above and varied in test cut-offs (standard vs. culturally adjusted), use of domain average cut-offs (no average vs. average), and use of TMT (inclusion vs. exclusion). Model accuracy was assessed using the F1 score (which balances precision and sensitivity). F1 scores >0.9 are considered excellent, >0.8 are good, and those between 0.6-0.8 are acceptable.

Result: The two models that used culturally adjusted cut-offs and domain average cut-offs performed best (with TMT, F1 = 0.81; without TMT, F1 = 0.78). All other models had F1 scores ranging from 0.63-0.73. While the two best models were similarly accurate based on F1 scores, the difference in the frequency of diagnoses between them was notable. In the model that excluded TMT, 38% of the sample were classified as Unaffected vs. 30% in the model that included TMT; (41% and 47% were classified MCI, respectively).

Conclusion: Our findings show that culturally sensitive adjusted cut-offs and inclusion of domain average cut-offs improved the accuracy of decision tree model classifications. These findings suggest that implementation of decision tree model approaches in the READD-ADSP dataset can accurately and efficiently classify individuals. Finally, we plan improve overall accuracy by using a hybrid method where models classify individuals who are clearly Unaffected or AD while more complex cases will be assigned to human adjudication.