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Background: Weight gain (WG) is a well-described phenomenon in PWH starting or switching ART. Machine learning (ML) methods is a tool of P4 medicine (Predictive, Preventive, Personalized & Participatory) and can generate models to identify patients at risk of WG. The objective was to develop an ML algorithm that predicts a 9-month WG≥ 5% in PLWH switching to InSTI with/without TAF. Methods: This was an observational study that comprised ART-experienced PWH attending Modena HIV metabolic clinic from 2004 to 2020. The patients' medical, HIV and ART data were partitioned in an 80/20 training/test set to generate predictive models. A ML model was used to leverage a hybrid approach where clinical expertise is applied along with data-driven analysis. The study outcome was the prediction at 9 months of weight change with a cut of 5%: at any patient visit (model 1) and in the subset of PWH switching to InSTI with/without TAF (model 2). 9-month prediction was chosen as being the minimum time occurring between any two given visits in the 95% of the cases. A robust implementation of linear regressor algorithms were able to predict weight gain/loss while tolerating missing data. Intelligible explanations were obtained through Shapley Additive exPlanations values (SHAP), which quantified the positive or negative impact of each variable included in each model on the predicted outcome. A measure of effectiveness (E-measure) was chosen as a performance metric, because unlike accuracy it can penalize errors, particularly underestimation ones. Results: A total of 2817 patients contributed to generate 10877 observations, which …