Despite lower extremity fractures being common injuries, little is known about how patient weight-bearing behavior during rehabilitation contributes to long-term outcomes. Monitoring patient weight-bearing behavior using wearable sensors would allow clinicians to develop data-driven rehabilitation protocols. The objective of this study was to categorize gait parameters based on their ability to differentiate between patients with excellent and average long-term outcomes using Fuzzy Inferences System (FIS).Methods: Patients with closed tibial or bimalleolar ankle fractures were recruited in this 3 year observational study. An insole load sensor continuously monitored patient weight-bearing during rehabilitation. Longitudinal data was reduced to 93 gait parameters. Using the 1 yearphysical function outcome score patients were divided into two groups; Excellent Outcomes, and Average Outcomes. A FIS classified gait parameters based on their ability to differentiate between the two outcomes.Results: Of the 42 patients enrolled, 17 had both 1 year physical function outcome score (9 Average, 8 Excellent) and complete insole data (33.7+14.5 y/o, 60% female). The FIS revealed that gait parameters related to step count and active walking time best differentiated the two outcome groups. Weight-bearing magnitude moderately differentiated the two groups, and cadence and static loading variables did not have strong differentiation. All metrics with strong FIS classification had statistically significant two-tailed T-test results (P-value < 0.03), while weak FIS differentiated groups did not.Conclusion: FIS proved to be a powerful tool for automated gait parameter classification due to its ease of implementation, adaptability, and intuitive graphical inputs. Although the data came from a pilot study with small patient size, FIS implementation indicated what gait patterns to focus on when designing higher-powered future clinical trials to produce data-driven protocols.