AI-Based Diagnostic Framework for Recommending Targeted Healthcare Solutions

The modern health information management ecosystem accumulates vast healthcare data containing crucial insights. With the escalating prevalence of chronic Non-Communicable Diseases, the imperative for timely diagnosis is critical. However, traditional diagnostic models struggle with the latency between symptom onset and professional intervention. This chapter elucidates the design of an Intelligent Healthcare System leveraging a hybrid data mining architecture. The framework integrates the LAMSTAR neural network for rapid symptom pattern matching with K-Means Clustering for granular disease categorization. Unlike conventional systems, this model enables dynamic, multi-symptom analysis to generate probabilistic diagnostic insights and incorporates a specialist recommendation module, functioning as a digital triage tool. This capability empowers patients with early health awareness and offers managerial value by optimizing resource allocation. The chapter concludes by discussing socio-technical challenges, data privacy, and empirical validation.