## 3 Methodology
### Feature Selection  
As reported in Table 1, our dataset has more than 400 attributes. This data phenomenon is known as the _curse of high-dimensionality_, whereby data points are sparse in the high-dimensional space, resulting in a negative impact on the predictive accuracy of machine learning models [51].  
Due to the extremely high dimensionality of our dataset, a rigorous feature selection process is implemented to enhance the predictive accuracy of the machine learning models. This process entails creating various versions of the dataset by systematically excluding chemical compound features that exhibit relatively low cumulative percentages across all essential oil samples.  
Each chemical compound could theoretically achieve a maximum percentage of 100% within a single essential oil, yielding a total possible sum of 2100% across all 21 essential oils. In practice, the highest observed cumulative percentage was for Limonene, with a value of 114.97%. To determine which features to retain, percentage thresholds were established based on this maximum value. 11 distinct threshold values, particularly 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 30, 40, and 50, were enforced, resulting in 11 dataset variations (subsets). Additionally, the original dataset, incorporating all chemical compound features (where the sum is greater than 0), is also included for comparison. This approach results in twelve unique datasets, each progressively refining the feature set to enhance the model's efficacy  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Essential Oil** & **Camphene** & **Limonene** & **Linalool** & **\(\alpha\)-Pinene** & **\(\beta\)-Pinene** & **1,8-Cineole** & **\(\ldots\)** & **Activity** \\ \hline Amla & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & LOW \\ \hline Anjeer & 0 & 0 & 0 & 0.6 & 0 & 0.2 & \(\ldots\) & HIGH \\ \hline Apricot & 0 & 2.54 & 6.38 & 1.37 & 0 & 0 & \(\ldots\) & HIGH \\ \hline Avocado & 0 & 0.1 & 0.01 & 0.67 & 0 & 0.3 & \(\ldots\) & LOW \\ \hline Chamomile & 0 & 0.3 & 0.3 & 1.9 & 0.1 & 0.1 & \(\ldots\) & HIGH \\ \hline Costus & 4.96 & 0.51 & 0.4 & 1.02 & 0.04 & 1.73 & \(\ldots\) & HIGH \\ \hline Ginger & 32.79 & 0 & 0.59 & 18.05 & 2.95 & 0 & \(\ldots\) & HIGH \\ \hline Ginseng & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & LOW \\ \hline Graprefruit & 0 & 94.2 & 0 & 1 & 2.17 & 0 & \(\ldots\) & LOW \\ \hline Gum Myrrh & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & LOW \\ \hline Hazelnut & 0 & 3.9 & 0 & 0 & 0 & 1 & \(\ldots\) & LOW \\ \hline Henna & 0.13 & 0 & 1.58 & 0.16 & 0.54 & 0 & \(\ldots\) & LOW \\ \hline Juniper & 0.9 & 12.1 & 0 & 29.1 & 17.6 & 0 & \(\ldots\) & LOW \\ \hline Mint & 0 & 0 & 2.22 & 0.69 & 1.14 & 0 & \(\ldots\) & HIGH \\ \hline Mustard & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & LOW \\ \hline Onion & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & LOW \\ \hline Rosemary & 11.38 & 1.32 & 0.25 & 20.14 & 6.95 & 26.54 & \(\ldots\) & HIGH \\ \hline Sadab & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & LOW \\ \hline Sandal & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & HIGH \\ \hline Sweet violet & 0 & 0 & 3.06 & 1.31 & 0.62 & 1.92 & \(\ldots\) & LOW \\ \hline Turmeric & 0 & 0 & 0 & 0 & 0 & 0 & \(\ldots\) & LOW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data representation of 21 essential oils and only some of their chemical compositions in percentages.  
in predicting the activity of essential oils against Alzheimer's Disease progression, from a computational perspective.  
Table 2 specifies the 11 threshold values enforced in the feature selection process, the corresponding number of resulting chemical compound features, and the designated names for each dataset. The experiments in Section 4 are carried out on all the 11 + 1 (original) datasets.