# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase
In addition, the AD-CR rules can be used by the clinicians during the clinical session to identify factors than may impact the disease or the disease advancement. Yet the evaluation of these rules by a specialised neuropathologist will need to be assessed in a separate research work. One of the limitations of this study is that it only evaluates the items of the ADAS-13 cognitive assessment method. Considering more than one cognitive assessment method would allow the data-driven classification model to cover a wider scope in terms of cognitive domains related to Neurocognitive Disorder, as defined in the DSM-5 framework. In addition, the study is limited to disease progression--it does not consider the passage of time. Considering the time elapsed between two successive dementia levels may help clinicians to develop specific intervention plans that are more appropriate to patients and their dementia level. Thereby, in the near future, we will investigate the differences in the disease progression between prodromal dementia, and other dementia levels (mild dementia, etc.). Lastly, we would like to discuss the rules generated by the proposed rule-based algorithm with a pathologist to seek his/her feedback on how these rules can be exploited to enhance the intervention and the diagnostic process of dementia. ## 6 Conclusions

The primary dementia condition is Alzheimer's disease (AD), which is typically diagnosed by a specialised clinician based on a set of criteria, including the scores of cognitive assessments that are designed to measure the patient's cognitive abilities besides other pathological assessments. One of the challenging problems related to dementia is determining the symptoms of the disease advances and the cognitive items that may be used as symptoms of such an advancement. This research investigated this problem based on real data related to neuropsychological assessment items by using machine learning to identify models with few cognitive items that influence the progression of AD--this to produce fast and accurate classifications. We proposed an enhanced covering classification algorithm called an Alzheimer's Disease Class Rules (AD-CR) that derives models that can be exploited by clinicians during the dementia screening process. The AD-CR algorithm is used to predict the progression of the disease for individuals undertaking a neuropsychological assessment based on models derived from real data. These classification models indicate associations among assessed items. They can thus be utilised as a digital information sheet to guide clinicians in diagnostic-related decisions. Empirical results obtained by dissimilar classification techniques against datasets related to cognitive items from the ADNI repository revealed that the AD-CR algorithm produced classification models that are competitive in terms of accuracy, specificity, and sensitivity rates. Moreover, the models derived by the classification algorithms are highly competitive to ANN, SVM, statistical, rule induction, and probabilistic approaches. The results demonstrated that few cognitive items may be used for screening dementia progression as models derived by the AD-CR algorithm from 3 to 6 cognitive items are predictive. After analysing the classification models derived, there were some associations, albeit low, between cognitive items that can be captured during the progression of AD. For example, word recall, word delay, word-finding, and language comprehension. These correlationscan be utilised within neuropsychological methods to retain fewer cognitive items with the least overlapping, which is beneficial for clinicians conducting clinical assessments for dementia progression. Analysing the classification models in regard to the impact of demographics attributes revealed that age contributes the most critical factor followed by gender and then education level at least when using the datasets, and methods considered.