# Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mid-Level Classification (Mean**} & \multicolumn{4}{c}{**Logistic Regression**} & \multicolumn{4}{c}{**Support Vector Classification**} & \multicolumn{4}{c}{**Random Forest**} & \multicolumn{4}{c}{**XGBoost**} \\ \cline{2-13}  & **Ji** & **WJi** & **Score** & **Ji** & **WJi** & **Score** & **Ji** & **WJi** & **Score** & **Ji** & **WJi** & **Score** \\ \hline
**Model Score** & 0.738 & 0.75 & 0.734 & 0.746 & 0.767 & 0.743 & 0.767 & 0.781 & 0.746 & 0.901 & 0.977 & 0.896 \\
**Precision** & 0.744 & 0.735 & 0.733 & 0.762 & 0.812 & 0.684 & 0.769 & 0.786 & 0.683 & 0.769 & 0.713 & 0.55 \\
**Sensitivity** & 0.728 & 0.729 & 0.728 & 0.687 & 0.702 & 0.714 & 0.617 & 0.689 & 0.674 & 0.733 & 0.731 & 0.503 \\
**Specificity** & 0.747 & 0.746 & 0.733 & 0.788 & 0.829 & 0.676 & 0.815 & 0.8 & 0.689 & 0.774 & 0.704 & 0.605 \\
**Accuracy** & **0.738** & 0.737 & 0.731 & 0.737 & **0.765** & 0.695 & 0.716 & **0.744** & 0.682 & **0.754** & 0.718 & 0.554 \\
**F1 Score** & 0.729 & 0.724 & 0.723 & 0.716 & 0.746 & 0.69 & 0.68 & 0.726 & 0.675 & 0.748 & 0.72 & 0.523 \\
**AUC** & 0.838 & 0.846 & 0.806 & 0.799 & 0.807 & 0.79 & 0.831 & 0.843 & 0.783 & 0.754 & 0.718 & 0.554 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Training and verification results of _ALS_EC, ALS_CG and _ALS_SELECT_FEA\({}_{1}\)_ (mid-level classification). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Individual Level Classification**} & \multicolumn{4}{c}{**Logistic Regression**} & \multicolumn{4}{c}{**Support Vector Classification**} & \multicolumn{4}{c}{**Random Forest**} & \multicolumn{4}{c}{**XGBoost**} \\ \cline{2-13}  & **Ji** & **WJi** & **Score** & **Ji** & **WJi** & **Score** & **Ji** & **WJi** & **Score** & **Ji** & **WJi** & **Score** \\ \hline
**Model Score** & 0.753 & 0.785 & 0.746 & 0.75 & 0.776 & 0.736 & 0.799 & 0.794 & 0.759 & 0.912 & 0.963 & 0.919 \\
**Precision** & 0.76 & 0.789 & 0.754 & 0.76 & 0.78 & 0.739 & 0.752 & 0.795 & 0.794 & 0.698 & 0.765 & 0.733 \\
**Sensitivity** & 0.744 & 0.773 & 0.715 & 0.744 & 0.719 & 0.715 & 0.702 & 0.759 & 0.688 & 0.673 & 0.773 & 0.676 \\
**Specificity** & 0.761 & 0.788 & 0.747 & 0.761 & 0.787 & 0.732 & 0.76 & 0.802 & 0.817 & 0.718 & 0.761 & 0.731 \\
**Accuracy** & 0.752 & **0.781** & 0.731 & 0.752 & **0.753** & 0.724 & 0.731 & **0.781** & 0.752 & 0.696 & **0.767** & 0.704 \\
**F1 Score** & 0.747 & 0.778 & 0.724 & 0.747 & 0.742 & 0.718 & 0.721 & 0.775 & 0.731 & 0.679 & 0.768 & 0.698 \\
**AUC** & 0.821 & 0.832 & 0.8 & 0.796 & 0.809 & 0.773 & 0.811 & 0.841 & 0.797 & 0.696 & 0.767 & 0.704 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Training and verification results of _ALS_EC, ALS_CG and _ALS_SELECT_FEA\({}_{1}\)_ (mid-level classification). Tables 9 and 10 show the various verification results for \(ALS\_EG\), \(ALS\_CG\), and \(ALS\_SELECT\_FEA_{0.1}\). It can be observed that the four machine learning models based on the WJI generally outperformed the JI and traditional scoring analysis. Table 9 is the prediction result obtained from using the individual disease codes for model training. For model training, the four different machine learning models incorporating the WJI yielded scores better in terms of precision, accuracy, and AUC values compared with the JI and traditional scoring analysis. Table 10 shows the results obtained using the middle-clustered-level classification for model training. The SVC and random forest learning models based on the WJI, rather than the JI and traditional scoring indicator, achieved better performance. The comparison of the performance of the WJI-based training models for the individual disease code and the middle clustered-level classification for the four different machine learning models are shown in Tables 9 and 10. For the mid-level clustered classification, the SVC learning model achieved a high accuracy of 0.765, sensitivity of 0.702, specificity of 0.829, and AUC value of 0.807.