# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase  
The rules algorithm pseudocode is depicted in Algorithm 1. The inputs are the classification dataset the \(\min\text{supp}\), and \(\min\text{conf}\) (See terms 6-9 above). The AD-CR algorithm deals with both categorical and continuous attribute values in the training dataset; any missing attribute values are removed prior to the training phase since the application data we are dealing with is medical. The \(\min\text{supp}\) threshold is employed to determine rule_items that have sufficient frequency in the training data and is mainly used to pinpoint the best frequent rule_item in any iteration. In doing so, only the best rule_item in terms of frequency is chosen each iteration by the AD-CR algorithm to start building a new rule or to append into the current rule's body. The algorithm keeps merging attribute values into the rule until the current rule passes the \(\min\text{conf}\) threshold; when this happens, the current rule is added to the candidate rules list. The confidence of the rule is calculated according to definition 8 and can be considered a performance indicator that reflects the rule's position during the learning phase. In discovering the rules (first phase), the AD-CR algorithm iterates over the training data to discover the best one rule_item (1-rule_item) in terms of support. It should be noted that only the largest class label in the training data occurring with the attribute value of the rule_item is considered when counting the rule_item's support. Once the 1-rule item is identified, the AD-CR algorithm starts building the first potential rule as \(\text{Best\_Item}\)\(\rightarrow\) C. The algorithm evaluates the current rule's confidence. If the current rule has confidence larger than the \(\min\text{conf}\) threshold, then it will be added to the candidate rule list, and all data examples associated with it will be removed from the training dataset. However, if the current rule's confidence is less than the min-conf threshold, then the learning algorithm isolates its training examples into a data structure: DS. The learning algorithm then checks whether adding the best frequent item of DS into the current rule will improve its confidence value. If this check yields true, then the algorithm appends the frequent item found into the current rule's body and repeats the same process of potentially adding frequent items into the rule's body from DS until the current rule's confidence passes the min-conf threshold. When this occurs, the rule will be generated and added to the candidate rule list. And all the rule's data examples will be removed from the original training dataset whenever the rule is generated, ensuring that these examples are only used once during the training phase. The algorithm then starts creating the second potential rule from the updated training dataset after removing the first rule's data examples and repeats the same process described above until either the training dataset empties or no more potential rules can be discovered. Once all candidate rules are generated, they are sorted based on confidence and support values. ```
Input: A classification dataset D, the min-supp and min-conf thresholds Output: Candidate Rule (CR): If-Then interpretable rules
1. For each rule_item (RI), i.e. \(([(Attribute,Attribute_{\



Another possible advantage of the training algorithm is that it does not seek rules with 100% accuracy as found in classic covering classification algorithms or for specific rules as found in enhanced rules algorithms. AD-CR permits the rules to be produced even when the rule's accuracy is not perfect at a good confidence level, thus reducing the chance of any overfitted predictive models. It should be noted that models that are generated by the AD-CT will be used for predicting the class of test data. ### Classification Phase

The AD-CR algorithm proposes a simple yet influential classification method that uses the most suitable rule to assign an appropriate class label to the test data during the classification phase.