AI-Driven-IoT(AIIoT) Based Decision Making System for High-Blood Pressure Patient Healthcare Monitoring

People with high blood pressure can now better monitor and manage their condition because of technological advancements and the accessibility of a wide range of monitoring equipment. People with high blood pressure must take a proactive part in caring for themselves and collaborate closely with their assigned medical professionals to keep their blood pressure under control. People who follow these principles can have happy and healthy lives, even with an illness. In addition to promoting patient empowerment, the Internet of Things (IoT) solution powered by artificial intelligence (AI) allows patients to view their health data in real-time. Patients can keep track of their vital signs using an easy-to-use smartphone app, which includes blood pressure measures, levels of physical activity, and other important indicators. Patients benefit from this because information allows them to make informed decisions about their lifestyle and drugs, and it also helps them to take a dynamic role in their health care. Another advantage of this system is its capacity to help healthcare providers make quick and correct decisions. AI-powered systems are capable of analyzing massive amounts of data and providing useful insights to healthcare providers. This could lead to various outcomes, such as an earlier diagnosis, more personalized treatment plans, and better hypertension control. The AI-Driven IoT based decision making (KSK1 approach) for blood pressure patient healthcare monitoring, which is powered by AI and built on the IoT, has the potential to alter how hypertension is handled dramatically. It enables precise and real-time patient monitoring, personalization of treatment regimens, patient empowerment, and the ability of medical professionals to make decisions based on credible data. We should expect significant improvements in the management of hypertension and other chronic disorders as this technology advances. Three measures are used to determine classifier effectiveness. Measurements include accuracy, precision, and recall. In the field of healthcare supplied by BP, the KSK1 approach that is currently in use provides an accuracy rate ranging from 89% to 92% for all conditions.