This study investigates the application of machine learning techniques for predicting stroke risk factors, a critical aspect in preventive healthcare. Stroke, a leading cause of disability and mortality worldwide, is often associated with various risk factors such as hypertension, diabetes, smoking, and obesity. Traditional risk assessment methods may lack accuracy and scalability, prompting the exploration of machine learning models for more effective prediction. Leveraging large-scale healthcare datasets, including electronic health records and demographic information, we develop and evaluate machine learning algorithms to predict the likelihood of stroke and identify significant risk factors. By harnessing advanced data analytics and predictive modeling techniques, our approach aims to facilitate early detection and intervention strategies, ultimately improving stroke prevention and patient outcomes.

This study investigates the establishment of failure thresholds to optimize the performance of rolling element bearings. Rolling element bearings are critical components in various mechanical systems, and predicting their failure is essential for preventing costly downtime and maintenance. Through empirical testing and analysis, this research aims to determine the specific operational conditions and parameters that lead to bearing failure. By identifying these failure thresholds, engineers can implement proactive maintenance strategies, such as condition monitoring and predictive maintenance, to extend bearing lifespan and minimize unexpected failures. The findings of this study provide valuable insights into enhancing the reliability and efficiency of mechanical systems by setting appropriate failure thresholds for rolling element bearings.