# Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients
## 4 Experiments and Results Analysis
### Experiments
#### 4.3.1 Regression and Classification Model  
Initially, we train our regression model only using \"misdiagnosed = NO\" class data instances to be able to efficiently predict the MMSE score values. To optimise the model testing process on balanced classes (\"misdiagnosed = \"NO\"/\"YES\"), we split the \"misdiagnosed = NO\" cases set into 5 folds. Each time, 4 different folds are used for training and the remaining one is merged with 133 data points of \"misdiagnosed = YES\" class for testing. This procedure is repeated five times, considering a different fold of \"misdiagnosed = NO\" class with the 133 \"misdiagnosed = YES\" class cases each time. This dictates that the regression will be tested using all possible cases of \"misdiagnosed = NO\" class, in order to ensure the data is thoroughly examined. After each repetition, predicted MMSE values of the data points are used to instantiate the baseline rule by experts (Section 3.2.1) and re-assess their classes (\"misdiagnosed = \"YES\"/\"NO\"). The performance of the predictive model is summarized in Tables 1 and 2, in terms of classification results (based on the original classification of MMSE scores) and confusion matrices, respectively. The average of all obtained matrices after five training repetitions is used to compute the average classification report and confusion matrix.  
to seriously affect the new classification of those cases. Thus, we first train the regression model, in order to learn the approximate function to predict \"misdiagnosed = NO\" patient's MMSE values, and thus, minimise the regression error for \"misdiagnosed = NO\" and maximise for the \"misdiagnosed = YES\" patients.  
Table 1 demonstrates that for originally \"misdiagnosed=YES\" cases, the resulting classification has a low recall and significantly high precision in average for all folds. The low recall score means that the regression model manages to \"correct\" more than half of the 133 originally misdiagnosed cases (77.6 Original:YES/Predicted:NO in average) as shown in Table 2. Thus, an significant percentage of previously misdiagnosed episodes, now have a more realistic MMSE score provided by our model.  
On the other hand, only a few originally correct cases out of 133 are now classified as misdiagnosed, based on the predicted MMSE scores (4.2 Original:NO/Predicted:YES on average). This is also demonstrated in the high recall of the \"misdiagnosed = NO\" cases. This observation means that 98% of the correct diagnosis cases did not turn into \"misdiagnosed = YES\" using the predicted MMSE scores. Therefore, the model has managed to correct a significant number of previously \"misdiagnosed = YES\", whilst not seriously affecting the \"misdiagnosed = NO\" class.  
Many attributes may impact the course of the predictive model. However, we illustrate the most important features in Table 3. As expected, most of these variables are related to individual aspects of the MMSE examination, and therefore, directly influences the prediction of MMSE value. We also illustrate other important features in Table 4 that are not related to the MMSE score questionnaire. These are also important, as they are the parameters highly contributing to the MMSE score estimation, without being directly part of the examination.