Artificial Intelligence-Driven Predictive Analytics for Early Detection of Chronic Diseases in Primary Healthcare

To discover patterns and risk factors that are associated with chronic illnesses including diabetes, hypertension, and cardiovascular difficulties, the objective of this study is to investigate these connections. The usage of machine literacy algorithms and big datasets will be how this objective will be realized. To develop prophetic models that can relate early warning indicators, the procedure involves integrating patient data and electronic health records (EHRs). This is done to train the models. According to the findings, AI-driven approaches have the potential to greatly increase the delicacy and punctuality of judgments, which will finally result in the improvement of patient troubles and the reduction of expenses linked with healthcare. In addition to this, the paper includes a consideration of the ethical and practical challenges that are associated with the application of such technologies in the context of standard clinical practice. It places a focus on the significance of having robust data governance and continual model confirmation. This paper draws attention to the transformative potential of artificial intelligence in primary healthcare and urges for its abandonment to proactively treat chronic diseases and reduce the burden they place on society.