# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 2 Literature Review
This model uses the variable used by the proposed model as input and with the CDR-SB score as the class label. Machine learning algorithms, including ANN, Decision Tree, and support vector machine (SVM), are used to build the models. A descriptive analysis was then performed to describe the process of the classification of patients based on the classification model learnt by the classification algorithms. In this descriptive analysis, Decision Tree outcomes are transformed into easy-to-interpret rules. Finally, the visualisation model helps to seek useful characteristics of instances that are clustered in a descriptive step. It uses contrasting colours so that patients with dementia and other diagnostic indicators can be distinguished immediately. [PERSON] et al. [27] used classification techniques for two datasets, each consisting of participants who were classified as exhibiting 'normal ageing' to dementia. The first dataset consisted of 310 participants who were diagnosed according to clinical diagnosis criteria; the second dataset contained 272 participants who were diagnosed according to CDR scores: 0 for normal ageing, 0.5 for MCI, and 1 and above for AD. Both datasets initially contained 27 variables related to various cognitive test scores, but this number was reduced using a wrapper feature selection method. To evaluate the generated models, 5-fold cross-validation was used. The main comments from the authors are that the classification techniques used showed no statistically different ability in classifying the data, with the most difficult (least successfully classified) group being the middle group, alternatively MCI, or CDR = 0.5. [PERSON] et al. [28] applied machine learning techniques to identify dementia from the National Health Service (NHS) data. A total of 26,483 data instances of individuals aged over 65 years were collected from 18 general practitioner surgeries. Machine learning techniques such as random forest, SVM, Naive Bayes, and linear regression were utilised to build classification models that were then evaluated using cross-validation testing methods with \(n\) = 10-fold in terms of specificity, accuracy, and sensitivity, among others. The dementia classification models derived by the machine learning algorithms pinpointed that the model derived by the Naive Bayes algorithm produced the highest performance. The classification model of Naive Bayes was able to detect 295 instances of dementia who had not received a clinical diagnosis. [PERSON] et al. [6; 29] studied real data related to functional elements of dementia from the ADNI project to determine which functional items may be signs of disease advancement. In [6], multiple dementia diagnosis methods were studied, and then a comparative analysis based on dissimilar criteria was carried out to seek a method that may cover more cognitive domains according to the DSM-5 framework. Later on, [PERSON] et al. [29] utilised a computational intelligence approach to derive impactful functional elements related to dementia progression. The authors used a number of classification techniques with feature selection within a data-driven approach that was implemented and tested on real cases and controls from ADNI's project and that had taken the Functional Activity Questionnaire assessment (FAQ). The results showed that there are a few functional items that can be observed during disease advancement. A mapping to these results were attempted by the authors and others in [30]. Table 1 depicts the summary of the relevant research conducted using specific criteria.