## 6 Conclusions and Future Work  
This study was conducted in the hope of bridging the gap between traditional knowledge of essential oils and modern machine learning techniques and offering a unique perspective on Alzheimer's Disease therapeutics. Particularly, this study investigates the predictive capability of machine learning techniques on the impact of plants' essential oils on Alzheimer's Disease progression.  
We curated data from 21 essential oils commercially available in the United Arab Emirates market. The 21 essential oil samples have a total of 428 chemical compounds, which, from a data analytics perspective, result in an extremely high-dimensional dataset that renders predictive models useless due to data sparsity. Extensive data preprocessing and feature selection steps were carried out in order to increase the efficacy of the applied machine learning models.  
Several classification models were configured and evaluated against two accuracy metrics. Experimental results showcased promising accuracy in predicting essential oils activity against Alzheimer's Disease progression, and suggest that Random Forest and Logistic Regression have the potential to be nearly 82% accurate.  
We emphasize that our findings require further in vivo and clinical studies are necessary to validate these findings and to assess the efficacy and safety of these compounds in human populations. This is an important and natural progression to this work.  
While our models exhibited robust performance, the exploration must continue. One potential future work is to validate the findings of this study clinically to measure the effectiveness and safety of essential oil compounds in human populations. Another future work is to employ and discuss more feature selection and model validation techniques and compare their impact on prediction capability. A third future direction would be employing data augmentation techniques and integrating neural networks to unravel complex relationships within chemical features. This holistic approach aims to enhance model generalization and uncover nuanced patterns, contributing to a more comprehensive understanding of essential oils' efficacy in preventing Alzheimer's Disease progression and exploring potential treatments.  
Conceptualization, R.M.A., N.S.A. and K.A.-H.; Methodology, R.M.A., K.A.-H. and J.J.K.; Software, J.J.K.; Validation, K.A.-H. and J.J.K.; Formal analysis, K.A.-H., and J.J.K.; Investigation, J.J.K.; Resources, R.M.A.; Data curation, R.M.A. and N.S.A.; Writing--original draft, R.M.A. and J.J.K.; Writing--review and editing, K.A.-H., R.M.A. and N.S.A.; Visualization, J.J.K. and R.M.A.; Supervision, R.M.A. and K.A.-H.; Project administration, R.M.A. and K.A.-H. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by Research Fund 2023-24-1004 from Rochester Institute of Technology, Dubai.  
Not applicable.  
Data Availability Statement: The raw data that support the findings of this article are publicly available on GitHub at [[https://github.com/researchrepol/EssentialOilsDataset](https://github.com/researchrepol/EssentialOilsDataset)]([https://github.com/researchrepol/EssentialOilsDataset](https://github.com/researchrepol/EssentialOilsDataset)) (accessed on 30 May 2024).  
The authors would like to thank the reviewers for their constructive feedback that contributed to the enhancement of this study.  
The authors have no competing interest to declare that are relevant to the content of this article.  
The following abbreviations are used in this manuscript:  
FDA Food and Drug Administration  
CNS Central Nervous System  
SVM Support Vector Machine  
\(k\)NN \(k\)-Nearest Neighbor  
LR Logistic Regression  
RF Random Forest