Background: The main objective of healthcare system is at once to enhance the quality of treatment and to have a check on the consumption of time and cost. Improving the quality of the health care system is to adapt to a semantic system, which is more easily understood by man and machine than the syntactic based system. Objective: In this research work, a framework for personalized decision support system is analyzed. To illustrate the framework, the well known medical decision-making problem of the thyroid gland and obesity is used. Methods: The developed framework constructs a fuzzy decision tree and the fuzzy rules are created from fuzzy decision tree. Then a medical expert evaluates the rules involved in the decision making process. Based on the decision, the system triggers the Semantic Web Rule Language (SWRL) that produces a treatment procedure. Results: The results of the framework provide the user-friendly environment for nurses, physicians and patients. The derived facts are automatically updated into ontology. Therefore it creates a dynamic process. The main goal of the developed framework is to provide personalized treatment flow without the intervention of domain experts. The implemented architecture provides good performance with respect to memory. The proposed automatic food recommendation system acts as a helping hand for people by saving many medical formalities and also reducing the time of domain experts. Conclusion: The proposed framework has the ability to automatically trigger the rules and make the treatment recommendations. Therefore it reduces the required time of care providers, patients and saves many medical resources.