# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase
Overall, the performance metrics' results improved for all the classification algorithms besides AD-CR when including demographics in the cognitive subsets. While the AD-CR algorithm also improved, though not as significantly (at least when processing 'Cog-subset1'), it was still superior in comparison to the other algorithms overall as it further elevated, reaching a performance above 92%, 92.38% accuracy, 91.30% sensitivity, and 93.50% specificity. The fact that the AD-CR algorithm derived classification models from all distinctive cognitive subsets with predictive accuracy above 90.00% is evidence that this algorithm is suitable for detecting dementia stage progression. The AD-CR algorithm produced models from all distinctive cognitive subsets with sensitivity and specificity rates of over 90.00%--except a specificity of 89.50% from 'Cog-subset3', which contains only three cognitive items plus demographics. In general, the Nnge algorithm derived competitive classification models on most cognitive subsets and above 90.00% accuracy rate. The classification models' performance derived by the AD-CR and Nnge algorithms from just six features ('Cog-subset4'), particularly their accuracy, sensitivity, and specificity, are close to those derived by the same algorithm from the complete cognitive items set ('Cog-subset1'). One notable result was observed by the models produced by the LR algorithm from 'Cog-subset4' including demographics in which the accuracy, sensitivity, and specificity rates improved by 7.20%, 6.00%, and 8.30%, respectively, when compared with those derived by the same algorithm from 'Cog-subset1' with demographics. The same pattern was also observed in the accuracy and specificity rates of the NB algorithm, which improved by 7.09% and 29.82%, respectively, making the items in 'Cog-subset4' significant, especially when investigated with demographic attributes such as age and gender. In fact, age was the highest-ranked feature among demographics that correlated with the class label, followed by gender and education level, at least using the feature assessment results. #### 5.2.3 Strengths and Weakness

Overall, the models produced from 'Cog-subset4', which contains (WORDRECALL, DELAYWORD, WordRECOG, ORIENT, COMMAND, WORDFIND) in addition to demographic attributes by the machine learning algorithms show good performance and save time by only requiring assessment in 6 activities. While the AD-CR algorithm may not have produced the best results across all measures, its overall results are above 90%, falling within the performance range of other ADAS-cog validation studies [45; 46] and required the use of fewer features; thus, it can be classified as a good performing model. The model of the AD-CR algorithm is equipped with interpretable classification rules that can easily be understood by clinicians and novice users. These models offer the patient and their family answers as to which components have been signs of dementia progression. This is aligned with the patient's right outlined in the General Data Protection Regulation (GDPR), particularly the section on decision-making using automated algorithmic methodsand the \"right for an explanation [47,48]. The GDRP requires that for any data collected from a subject (the individual undergoing medical screening) in an automated decision process (the screening process using machine learning), the subject should have the right to be given the rationale behind the decision-making process. Consequently, having an intelligent and easy-to-understand screening system for the patients and their family members, besides clinicians, is valuable. The system can provide explanations to the different stakeholders and useful information in answering questions such as:

1. What are the cognitive features that relate to the progression of AD? 2. Why does the output of the screening show no progression or potential progression? 3. Why is the patient being screened for AD, MCI, or CN? 4. What further assessment can be made based on the outcome?