# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase
\begin{table}
\begin{tabular}{c c} \hline \hline
**IG** & **Feature** \\ \hline
**Score** & **WORDRECALL** \\ \hline
0.084 & DELAYWORD \\ \hline
0.062 & WORDRECOG \\ \hline
0.049 & COMMAND \\ \hline
0.044 & ORIENT \\ \hline
0.042 & WORDFIND \\ \hline
0.038 & IDEATIONAL \\ \hline
0.032 & NAMING \\ \hline
0.032 & CONSTUCT \\ \hline
0.028 & LANGUAGE \\ \hline
0.025 & SPOKENLG \\ \hline
0.021 & RMBRTESTINSTR \\ \hline
0.021 & NUMBERCANCEL \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cognitive Items with Ranked Scores Derived by the IG Method. sensitivity, specificity, and predictive accuracy, even when only three items were processed ('Cog-subset3'). Moreover, the AD-CR algorithm was superior to the other algorithms when processing the baseline and 'Cog-subset2' in terms of predictive accuracy. For instance, it was able to produce predictive models from the baseline subset with higher accuracy than these of LR, MLP, SMO, KNN, NB, Ridor, and Nnge with 13.12%, 7.36%, 14.18%, 0.87%, 23.93%, 2.56%, and 0.36%, respectively. In fact, the AD-CR algorithm was able to derive a model from just three cognitive items of the 'Cog-subset3' (word recall, delayed word recall, and

\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Including Demographic Features**} & \multicolumn{4}{c}{**Excluding Demographic Features**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Subset**} \\ \hline
**Specificity\%** & **Sensitivity\%** & **Accuracy\%** & **Specificity\%** & **Sensitivity\%** & **Accuracy\%** & \\ \hline
79.30 & 87.10 & 83.15 & 72.90 & 76.90 & 74.88 & LR \\ \hline
86.40 & 87.70 & 87.03 & 77.80 & 83.60 & 80.64 & MLP \\ \hline
76.70 & 88.60 & 82.59 & 70.70 & 77.00 & 73.82 & SMO \\ \hline
84.10 & 93.50 & 88.78 & 82.30 & 92.10 & 87.13 & KNN \\ \hline
35.18 & 94.50 & 70.54 & 34.30 & 94.40 & 64.07 & NB \\ \hline
94.50 & 86.10 & 90.32 & 91.10 & 79.70 & 85.44 & Ridor \\ \hline
92.80 & 91.60 & 92.22 & 88.40 & 86.90 & 87.64 & N



word recognition) with an 83.80% sensitivity rate, i.e., the model works relatively well. This rate is just 3.10% less than when the same algorithm processed the baseline cognitive items (Cog-subset1). It seems that using only three items from the ADAS can indeed help clinicians in detecting possible signs of disease progression, at least when using models generated by the AD-CR and other classification algorithms like KNN. These three items also commonly appear in the other derived subsets, underlining their importance during the dementia progression. Furthermore, the results revealed that albeit the KNN algorithm derived predictive models that were not the most accurate from the complete cognitive items and 'Cog-seubset2', it was able to derive models from 'Cog-subset3' and 'Cog-subset4' that were slightly better than the AD-CR. This supports the view that the cognitive items in these subsets are important for detecting any possible signs of disease advancement. Specifically, evaluating 'orientation', 'command', and 'word finding' in addition to the items of 'Cog-subset3' resulted in predictive models with a 3.10% and 4.24% increase in accuracy for the KNN and the AD-CR algorithms, respectively, when compared with models derived by the same algorithms from just three items ('Cog-subset3'). In addition, the Ridor and Nnge algorithms, which produce classifiers with rules, were able to produce classification models for dementia progression that were good in terms of accuracy from all cognitive subsets except 'Cog-subset3'. This suggests that rule-based classification is an appropriate machine-learning approach for the problem of predicting the disease's progression. We investigated the confusion matrix results to understand how these classification models behave in terms of performance.