# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 3 Prediction Model for Dementia Diagnosis
### Preprocessing  
The collected data includes data from patients who have problems with hearing and vision or who are unable to be examined due to anxiety, and data that is lost due to errors or omissions in the data collection process. Because only a few machine learning algorithms ignore missing value (e.g., Bayes, Neural Network) during data training and most algorithms can be affected by such gaps, we deleted missing values and errors using data preprocessing. Data preprocessing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the data. Data preprocessing involves finding erroneous, incomplete, irrelevant or corrupt data in a record set, table, or database, and then correcting or deleting the data. The missing \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**All Patients**} & \multicolumn{2}{c}{**Patients (MCI)**} & \multicolumn{2}{c}{**Patients (Dementia)**} \\ \hline \hline  & Range & \begin{tabular}{c} Descriptive \\ statistics \\ \end{tabular} & Range & \begin{tabular}{c} Descriptive \\ statistics \\ \end{tabular} & Range & \begin{tabular}{c} Descriptive \\ statistics \\ \end{tabular} \\ \hline \hline Gender & Male/Female & \begin{tabular}{c} Male: 501 \\ Female: 735 \\ \end{tabular} & Male/Female & \begin{tabular}{c} Male: 287 \\ Female: 376 \\ \end{tabular} & Male/Female & \begin{tabular}{c} Male: 214 \\ Female: 359 \\ \end{tabular} \\ \hline Age & 45–99 & \begin{tabular}{c} \(\mu=76.381\) \\ \(\sigma=7.624\) \\ \end{tabular} & 54–99 & \begin{tabular}{c} \(\mu=74.118\) \\ \(\sigma=6.894\) \\ \end{tabular} & 45–99 & \begin{tabular}{c} \(\mu=79\) \\ \(\sigma=7.596\) \\ \end{tabular} \\ \hline Education & 0–18 & \begin{tabular}{c} \(\mu=5.979\) \\ \(\sigma=4.71\) \\ \end{tabular} & 0–18 & \begin{tabular}{c} \(\mu=6.596\) \\ \(\sigma=4.592\) \\ \end{tabular} & 0–18 & \begin{tabular}{c} \(\mu=5.265\) \\ \(\sigma=4.747\) \\ \end{tabular} \\ \hline MMSE-KC & 0–30 & \begin{tabular}{c} \(\mu=17.715\) \\ \(\sigma=6.026\) \\ \end{tabular} & 0–30 & \
\begin{tabular}{c} \(\mu=20.021\) \\ \(\sigma=4.978\) \\ \end{tabular} & 0–28 &
\begin{tabular}{c} \(\mu=15.047\) \\ \(\sigma=6.035\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical Information for the Collected CERAD-K (Phase2). MCI: mild cognitive impairment. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**All Patients**} & \multicolumn{2}{c}{**Patients (Normal)**} & \multicolumn{2}{c}{**Patients (Cognitive Decline)**} \\ \hline \hline  & Range & \begin{tabular}{c} Descriptive \\ statistics \\ \end{tabular} & Range & \begin{tabular}{c} Descriptive \\ statistics \\ \end{tabular} & Range & \begin{tabular}{c} Descriptive \\ statistics \\ \end{tabular} \\ \hline \hline Gender & Male/Female & \begin{tabular}{c} Male: 4808 \\ Female: 919 \\ \end{tabular} & Male/Female & \begin{tabular}{c} Male: 2837 \\ Female: 6962 \\ \end{tabular} & Male/Female & \begin{tabular}{c} Male: 1971 \\ Female: 2230 \\ \end{tabular} \\ \hline Age & 30–106 & \begin{tabular}{c} \(\mu=73.07\) \\ \(\sigma=7.205\) \\ \end{tabular} & 30–101 & \begin{tabular}{c} \(\mu=72.392\) \\ \(\sigma=7.001\) \\ \end{tabular} & 42–106 & \begin{tabular}{c} \(\mu=74.651\) \\ \(\sigma=7.425\) \\ \end{tabular} \\ \hline Education & 0–25 & \begin{tabular}{c} \(\mu=6.21\) \\ \(\sigma=4.8\) \\ \end{tabular} & 0–25 & \begin{tabular}{c} \(\mu=5.909\) \\ \(\sigma=4.895\) \\ \end{tabular} & 0–22 & \begin{tabular}{c} \(\mu=6.913\) \\ \(\sigma=4.492\) \\ \end{tabular} \\ \hline MMSE-KC & 0–30 & \begin{tabular}{c} \(\mu=23.348\) \\ \(\sigma=4.8\) \\ \end{tabular} & 14–30 & \
\begin{tabular}{c} \(\mu=25.214\) \\ \(\sigma=3.098\) \\ \end{tabular} & 0–25 &
\begin{tabular}{c} \(\mu=18.995\) \\ \(\sigma=5.226\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}