# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase
For instance, for the AD-CR model produced from 'Cog-subset1', out of the 5923 positive instances that were supposed to have progression, the AD-CR algorithm was able to correctly predict 5147 instances (true positive) and misclassified 776 instances as 'no progression' (false negative), when these instances were in fact having progression. The ability of the model to predict progression correctly is significant, especially in the medical field. While using the AD-CR algorithm, the models derived from 'Cog-subset1' and 'Cog-subset2' produced the best accuracy measure; surprisingly, the NB probabilistic algorithm derived models from these two cognitive subsets with the highest sensitivity. NB algorithm derived models from the 'Cog-subset1' and 'Cog-subset2' with 93.50% and 94.40%, respectively--superior to the remaining classification algorithms, at least on the sensitivity metric. Nevertheless, the specificity rates derived by the NB algorithm from these datasets were unacceptable at 33.30% and 34.30%, respectively. In other words, the NB algorithm can detect disease advancement but with a high number of false positives. For example, by analysing the confusion matrices of the NB model against 'Cog-subset1', we discovered that out of 5923 positive instances, only 333 instances were misclassified by the NB algorithm as 'no progression'. However, there were 3957 false positives out of 6020 cases, which contributed to the low specificity rate. The algorithm was able to balance between specificity and sensitivity rates across all models derived from the distinct cognitive feature subsets. Specifically, the AD-CR derived models with sensitivity and specificity rates of 86.90%, 89.10%, and 83.80%, 86.60%, respectively, from the 'Cog-subset1' and 'Cog-subset2'. When comparing the sensitivity rates of the models produced by the AD-CR algorithm, it is evident that the model derived from the 'Cog-subset1' had the highest; the difference in percentages of AD-CR models across all other measures against 'Cog-subset3' is not significant, where only its specificity (77.40%) is below 80.00%. The fact that 'Cog-subset3', when processed by the AD-CR algorithm, can produce good results with only three cognitive items means less assessment is required. Furthermore, the models derived from 'Cog-subset3' achieved this despite this subset of data covering only two DSM-5-prescribed cognitive domains, in comparison to 'Cog-subset2' and 'Cog-subset4', which cover more cognitive domains, respectively. Interestingly, the overall performance of the classification techniques produced similar results as other validation studies where the accuracy ranged from 82-99.6%, sensitivity from 58-74% and specificity from 91-98% [45; 46]. Reducing the number of items in assessments related to dementia conditions, such as AD, can enable diagnosticians to evaluate specific elements related to a patient's condition based on the state of the disease, the patient's medical history, and the characteristics. Focus can then be on the cognitive elements detected as symptoms for the disease progression for individualised intervention and management plans to be designed to suit the patient and their family members. This will have positive impacts on their lives as well as on the healthcare system. We investigated the impact of adding demographic features (age, gender, level of education, race category, and marital status) to the cognitive items (right side of Table 5). The performance across all classification algorithms improved immediately, particularly evident in the SMO, LR, and MLP algorithms, with noticeable improvement across all performance measures. For example, the improvement in accuracy, sensitivity, and specificity rates of the LR models after considering demographics with cognitive items of 'Cog-subset2' increased 18.98%, 23.40%, and 14.60%, respectively, when compared with the model derived from 'Cog-subset2' without demographics.