Heart disease poses a significant risk to global health, and accurate prediction of this risk is critical to public health. This study leverages the Kaggle heart disease dataset, employs machine learning models to predict heart disease risk, and introduces sequence analysis to minimize sample collection while maintaining accuracy. Of the four models, linear regression, Bayesian ...

This study emphasizes the importance of monitoring and predicting plant functional traits for ecological and agricultural research, particularly in the context of climate change. Hyperspectral remote sensing is a key tool for studying vegetation health and ecosystem dynamics due to its efficiency and wide coverage. Common modeling methods include vegetation indices, partial least ...