An inevitable issue with integrative alternative healthcare systems (IAHS) is that although clinical reasoning is fuzzy, qualitative, and dependent on the practitioner\'s expertise, current evidence-based requirements mandate a precise and quantifiable approach. This paper proposes a new framework for decision making within IAHS using fuzzy logic by considering the fuzzy nature of Ayurvedic doshas, TCM meridians, and constitutional types within naturopathy. In this regard, we propose a hierarchical structure of fuzzy inference systems (FISs) containing 27 rules that map qualitative input statements (\"Vata moderately high,\" \"Qi slightly weak\") to recommendations of therapy along with an MCDM process using the Fuzzy TOPSIS algorithm for treatment prioritization. Applied to clinical information from 180 patients suffering from metabolic syndrome, our system yields a consensus with an expert panel of 86.4% (?=0.82), while decreasing variability in prescription by 58% in comparison with unassisted practitioners.