BACKGROUND : Medical tourism for infertility care is expanding, but the relative contribution of health system and service factors to destination desirability remains uncertain. This study aimed to identify and rank the determinants of medical tourism for infertility care and predict “desirability” destination using an artificial neural network (ANN). METHODS : A cross-sectional study at the Research Institute for Reproductive Sciences, Yazd, Iran (study period 2021–2023). Consecutive consenting medical tourists completed the validated medical tourists attracting factors (MTAF-46) questionnaire consisting of 10 domains. The primary outcome was destination desirability. An ANN (feed-forward, supervised learning) was trained on a random split of the dataset and evaluated on held-out data. Performance was summarized by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Variable importance was used to characterize the type, direction, and relative strength of associations. RESULTS : A total of 430 respondents were analyzed. The evaluation set showed a strong discrimination (AUC: 95.2%) with overall accuracy 91.3%, sensitivity 57.1%, and specificity 98.1%. Higher scores across several domains were positively associated with desirability; the top contributors were: medical services, economic factors, advertising and marketing, administrative and financial services, and support services. Class proportions indicated that “desirable” cases were less frequent (11.0% in the evaluation set), and model performance favored specificity, consistent with conservative positive classification. CONCLUSIONS The development of infertility medical tourism in Yazd is mainly driven by the quality of core clinical services and economic factors. Effective marketing, transparency in administrative and financial processes, and efficient support services also enhance the destination’s attractiveness.
advertising ; Iran ; marketing ; medical services ; medical tourism ; medical tourists