A Novel Approach For Prediction Of Healthcare Center Discovery Based On Recommendation System For Car Accidents For Ghana.

Personalized recommendation systems have transformed numerous industries, including healthcare, by enabling tailored solutions to user needs. This paper presents an innovative recommendation system designed to assist users in identifying appropriate healthcare centers through the integration of clustering and association mining techniques. By combining these methodologies, the system enhances the accuracy and relevance of recommendations while addressing the inherent challenges of healthcare center selection. Specifically, clustering is employed to group healthcare centers based on shared attributes, and association mining uncovers hidden patterns and relationships that improve recommendation quality. The proposed system's effectiveness is evaluated through empirical testing and performance benchmarking against conventional recommendation models. A significant focus is placed on overcoming challenges such as the Cold Start problem and data sparsity, which often limit the efficiency of recommendation systems. To address these issues, a hybrid approach is introduced, integrating data clustering with the Eclat Algorithm to produce more effective association rules. The process begins with clustering the rating matrix based on user similarities, followed by converting the clustered data into Boolean form for application of the Eclat Algorithm, resulting in a refined and optimized recommendation system.