Oleander: Feature Selection and Classification Method for Predicting Human Thirst

Human dehydration is a widespread and serious health issue that can lead to various problems such as headaches, dizziness, decreased cognitive function, and even heat stroke. Therefore, accurate prediction of dehydration is critical in preventing and treating this condition. In this study, we aim to provide a solution to this problem by proposing the Oleander algorithm for human dehydration prediction. The Oleander algorithm is a machine learning-based approach that incorporates advanced techniques and algorithms to improve the accuracy of dehydration predictions. To assess the algorithm's predictive accuracy, KNN (K-Nearest Neighbors) and SVM (Support Vector Machine) were compared with two others widely used machine learning techniques. Our research builds upon previous studies and advances the state of the art by incorporating novel techniques and algorithms. In our experiments, the Oleander algorithm outperformed the KNN and SVM algorithms in terms of prediction accuracy. Our methodology for implementing the Oleander algorithm is explained in detail, including the data pre-processing, feature extraction, model training, and evaluation procedures. We believe that our proposed methodology is suitable for deployment in real-world applications and will help to prevent and mitigate the adverse effects of dehydration by providing more accurate predictions of this condition. In conclusion, the Oleander algorithm is a promising solution for human dehydration prediction that provides higher accuracy compared to traditional machine learning algorithms. Our research will contribute to the field by advancing the state of the art in human dehydration prediction and providing a useful tool for healthcare professionals and individuals alike.