Predictive Analysis in Healthcare Systems
J. Sathya, F. Mary Harin Fernandez · 2024
Predictive analysis can profoundly transform healthcare systems by facilitating preemptive interventions, optimizing resource allocation, and enhancing well-informed decision making. Digital bullying has become a significant social problem with severe implications for the mental and emotional well-being of individuals. Addressing and preventing cyberbullying requires a collaborative effort from law enforcement agencies, policymakers, educators, and online platforms. A safer and more compassionate online environment can be achieved by working together and implementing comprehensive strategies. The biomedical support system (BSS) represents a significant advancement in healthcare technology by integrating advanced machine learning (ML) algorithms and natural language processing techniques, including social media platforms and messaging applications. The proposed system enhances decision-making capabilities by employing sentiment analysis and language pattern recognition and enables effective prevention and intervention in cyberbullying incidents. This proposed method represents a significant advancement in addressing cyberbullying crimes by merging biomedical knowledge with cutting-edge technologies. BSS empowers decision makers to respond to cyberbullying incidents and reduce their prevalence effectively. By leveraging natural language processing, machine learning, and social network analysis techniques, the system aims to provide valuable insights and assist in combating cyberbullying effectively. Machine learning empowers the BSS to combat cyberbullying effectively by processing and analyzing data, extracting meaningful insights, and providing decision makers with valuable information. With its ability to adapt to evolving cyberbullying tactics and contribute to developing proactive strategies, machine learning plays a crucial role in the fight against cyberbullying and in promoting a safer online environment.