# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase
\[Accuracy=\frac{TN+TP}{TN+FP+FN+TP} \tag{1}\]

\[Sensitivity=\frac{TP}{TP+FN} \tag{2}\]

\[Specificity=\frac{TN}{TN+FP} \tag{3}\]

\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset Name** & **\# of Patients before Sampling** & **\# of Data Observations (Visits)** & **DX Progress—Class Distribution before Data Balancing** & **DX Progress—Class Distribution after Data Balancing** \\ \hline ADNI-Merge-ADAS-Cog dataset & 1710 & 6330 & Total observations: 6330 & Total observations: 11,943 \(\text{\textasci{}}\) \(\textasci{}\) \(\textasci{}\) \(\textasci{}\) \(\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci}{\textasci}{ }\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasciasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasciasci}{}\) \(\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasciasci{}\text{}\textasciasci{}\textasci{}\textasci{}\textasci{}\textasciwhere,

_TP_ (True Positive) = The model predicts a positive outcome among those with the positive class;

_FP_ (False Positive) = The model predicts a positive outcome among those with the negative class;

_TN_ (True Negative) = The model predicts a negative outcome among those with the negative class;

_FN_ (False Negative) = The model predicts a negative outcome among those with the positive class;

To measure the effectiveness of the AD-CR algorithm, we used seven classification algorithms: Logistic Regression (LR), Multilayer Perceptron (MLP), Sequential Minimal Optimization (SMO), K-Nearest Neighbour (KNN; k = 5), Naive Bayes, Ripple-Down Rule learner (Ridor), and Non-nested generalised exemplars (Nnge) [36; 37; 38; 39; 40; 41; 42]. The reasons for choosing these classification methods are due to the following factors:

1. The dissimilarities in the learning mechanisms used by these algorithms;
2. The different types of classifier formats they offer;
3. Many of these methods have been used in medical-related research;
4. To assist in obtaining a general conclusion wherein we can compare with the AD-CR algorithms in terms of performance measures. MLP is a type of neural network that implements a feed-forward mechanism in the way it models the problem; the structure of the MLP comprises three layers: input, output, and hidden. The input layer consists of a set of neurons that represent the features in the training dataset, and it receives the data input that requires processing. The output layer performs the task of predicting the class label when the task is classification-based on computations made in the hidden layer. The algorithm keeps adjusting the model derived by amending the weights of the neurons until it reaches an acceptable performance level. LR is a statistical algorithm that, in its simplest form, can describe the relationship between two features of data. LR uses a logistic function, as shown in Equation (4), to model a class label with two possible values.