Hybrid Artificial Intelligence Using Decision Tree and Heuristic Optimization for Healthcare Facility Recommendation Systems
DOI:
https://doi.org/10.55927/fjsr.v5i5.32Keywords:
Hybrid Artificial Intelligence, Decision Tree, Healthcare Recommendation, Heuristic Optimization, Digital HealthAbstract
This study proposes a healthcare facility recommendation system using a Hybrid Artificial Intelligence approach. The model integrates Decision Tree classification to identify user healthcare needs and heuristic-based optimization to rank healthcare facilities based on geographic distance and facility density. The study employed the Waterfall development model and used 150 simulated healthcare scenarios. Experimental results showed that the proposed model successfully classified user healthcare needs into Clinic, Hospital, and Emergency Unit categories, while the heuristic model effectively identified optimal healthcare facilities among multiple alternatives. Compared with conventional location-based approaches, the proposed model demonstrated more adaptive and efficient recommendation performance. These findings demonstrate the potential of hybrid artificial intelligence to support adaptive, accurate, and efficient healthcare decision-making.
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