These predictions achieved an accuracy of 0.94, 0.94 and 0.96 with the Matthews correlation coefficient (MCC) of 0.53, 0.53 and 0.65 respectively for the three protein structure sets.
Our overall results on the test data for the 4 7 weeks in the future prediction achieved a PPV of 0.686, a NPV of 0.976, a sensitivity of 0.615, and a specificity of 0.982 and can be seen on Figure 16.
The weekly, T+3 (i.e., three-weeks in advance) incidence prediction achieved a PPV of 0.667, a NPV of 0.983, a sensitivity of 0.593, and a specificity of 0.987 on the test data set.
Nonetheless our highest confidence edge direction predictions achieve an accuracy of 64 67% using only non-time series observational data.
The method infers spatially proximal residue pairs in this test set with good accuracy: top-ranked predictions achieve an average accuracy of 38% (for an average 21-fold improvement over random predictions) in cross-validation tests.
For instance, Hossain et al. [4] developed a Bayesian belief network for real-time crash prediction and achieved a crash prediction accuracy 66% with the false alarming rate less than 20%.
All of our predictions achieved high AUC scores.
However, even if these actions did contain sufficient cues for infants to interpret them as goal-directed, there are still reasons to question the assumption that predictive eye movements reflect a goal prediction achieved by a motor process.
In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping.
These variables were aggregated to a prediction score, which achieved a prediction accuracy of 0.84 for AD.
