A Machine Learning Approach to Evaluating the Impact of Natural Oils on Alzheimer’s Disease Progression

Abstract

Alzheimer’s Disease is among the major chronic neurodegenerative diseases that affects more than 50 million people worldwide. This disease irreversibly destroys memory, cognition, and the overall daily activities which occur mainly among the elderly. Few drugs are approved for Alzheimer’s Disease management despite its high prevalence. To date, the available drugs in the market cannot reverse the damage of neurons caused by the disease leading to the exacerbation of symptoms and possibly death. Medicinal plants are considered a rich source of chemical constituents and have been contributing to modern drug discovery in many therapeutic areas including cancer, infectious, cardiovascular, neurodegenerative and Central Nervous System (CNS) diseases. Moreover, essential oils that are extracted from plant organs have been reported for a wide array of biological activities, and their roles as antioxidants, antiaging, cytotoxic, anti-inflammatory, antimicrobial, and enzyme inhibitory activities. This article highlights the promising potential of plants’ essential oils in the discovery of novel therapeutic options for Alzheimer’s Disease and halting its progression. In this article, 428 compounds were reported from the essential oils isolated from 21 plants. A comparative study is carried out by employing a variety of machine learning techniques, validation, and evaluation metrics, to predict essential oils’ efficacy against Alzheimer’s Disease progression. Extensive experiments on essential oil data suggest that a prediction accuracy of up to 82% can be achieved given the proper data preprocessing, feature selection, and model configuration steps. This study underscores the potential of integrating machine learning with natural product research to prioritize and expedite the identification of bioactive essential oils that could lead to effective therapeutic interventions for Alzheimer’s Disease. Further exploration and optimization of machine learning techniques could provide a robust platform for drug discovery and development, facilitating faster and more efficient screening of potential treatments.