# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase
A rule used to assign the class normally meets two conditions:

1. It has the best ranking among all other rules in terms of confidence and support values;
2. The attribute values in its body are contained within the test data, thus ensuring the similarity of the attributes' values. During the classification phase, when a test data example is to be classified, the AD-CR seeks in the final set the rule that matches the test data's attribute values, allocates its class to the test data example, and then moves to the next test data example and so forth. However, if there is no rule in the final rules set that fully matches the test data example, then the AD-CR algorithm searches for partially matching rules; such rules have at least one attribute data value similar to the test data example. The AC-DR algorithm then allocates the class linked with more partially matching rules to the test data example. This procedure ensures more than one rule can be employed to assign the class label to the test data, unlike other covering algorithms, which employ just the first partial matching rule. In cases when no rules partially or fully match the test data example, the algorithm uses the default rule one, which denotes the class label with most of the training data examples. Using just one rule for classifying test data examples is not only a simple approach, but it also provides good predictive power, as seen later in the experimental analysis section. In addition, the approach considers other rules that partially match the test data examples when no fully matching single rule is available rather than invoking the default class label. This reduces the number of arbitrary classifications and, thus, misclassifications. We summarise the primary characteristics of the AD-CR algorithm:

1. Only rules with significant frequency and confidence are formed;
2. Fewer rules are formed; thus smaller in size models are produced;
3. Unlike association rule mining, no rules share data examples, thus reducing the search space of items and potential rules;
4. Rules can be associated with some degree of error to minimise overfitting;
5. A simple and effective classification method is used in the prediction phase. ## 4 Data, Features and Pre-Processing

The datasets used in this research were obtained from ADNI. After careful investigation of the ADNI data repository, we identified the required features and combined multiple datasets. In particular, ADNI-Merge and ADAS-Cog-sheet cover the scope of the research project, and they include patients' cognitive information as well as visits. The ADNI-Merge dataset has a cohort of 2260 participants, in which each participant is being monitored on a six-monthly basis to track and study their AD progression, with multiple observations per patient at different points in time. The ADAS-Cog-sheet dataset comprises cognitive tasks that assess learning and memory, language production, language comprehension, constructional praxis, ideational praxis, and orientation. The ADAS-Cog sheet dataset details the patient's score in each cognitive task and the total scores attained during the assessment. It contained 6770 observations and 121 attributes. We are only interested in the cognitive tasks and their associated scores. There are two common attributes within each dataset that form the basis of the data integration of ANDI-Merge and ADAS-Cog sheet: the patient ID (RID) and the visit code (VICCODE), which will act as a reference for the merger. The aim of the merging process is to capture individual cognitive items together from their respective datasets and the diagnostic class (DX) from ADNI-Merge for each visit per patient. There were instances where a patient observation in the ADNI-Merge dataset was not integrated due to the ADAS-Cog-sheet dataset not having a corresponding RID and visit code. This could be due to the assessments not being performed for the patient during a visit for various reasons; thus, no merging occurs, resulting in fewer observations in the newly merged dataset.