Advancements in Immunization Management for Personalized Vaccine Scheduling with IoT and Machine Learning

Immunization is essential to public health; currently, comprehensive vaccine schedules may not sufficiently address individual health profiles. The Internet of Things (IoT) collects real-time health data, and decision trees (DTs) develop personalized vaccination schedules. Wearable health monitors and electronic health records provide continuous health data and information. DTs are customizable machine learning (ML) algorithms that assess this data and create immunization regimens. Based on age, vaccination history, underlying health issues, and real-time health measurements, DTs provides individualized vaccine recommendations. More vaccination compliance and greater health needs alignment are the system’s main benefits. Based on real-time data, this technique recommends vaccination administration timings. Optimization of vaccination systems reduces vaccine-preventable illness transmission. Integrating IoT data, preprocessing, and vaccination scheduling using DTs are covered in this work. It emphasizes data security and ethics while managing sensitive health information during privacy and security issues. Personal vaccination scheduling using IoT and DTs advances public health. Regulations that protect privacy and provide equal access to personalized vaccination schedules are essential as the system matures. This novel method might save lives, save healthcare costs, and improve vaccination systems.