## 4 Discussion  
Results showed that regardless of the validation technique used, the classification rates were higher when the male and the female population were considered separately. Indeed, using a 10-fold validation, the prediction performance reached 84.4% for men and 75.5% for women but it was limited to 71 % when the two sexes were grouped together. This confirms that prediction models should be sex-specific to achieve better results and supports previous studies highlighting the differences in kinematics between men and women [30; 31]. The analysis of the confusion matrices and the ROC curves showed that the specificities and the sensitivities led to accurate prediction models (i.e., 70% or higher). In both cases sensitivity (85.4% and 76.5% respectively for men and women prediction models) was better than specificity (70.3% and 70.7% respectively for men and women prediction models). Therefore, the models were better at predicting a not-Improved (nl) condition than an Improved (l) condition. Unlike many classification methods, decision trees are intuitive and provide a graphic, meaningful and easy-to-read representation compared to other well-known prediction and classification methods such as neural networks and support vector machine. The prediction model was enhanced by implementing a user-friendly graphical interface allowing clinicians to query patient characteristics and improve their understanding of the classification system's decision. The three discriminant features retained for the male prediction model were entirely different from the three chosen for the female model, confirming that sex-specific analyses were appropriate. Interestingly, among the 69 features that were measured on 3D kinematic curves at baseline, the features retained for both decision trees were closely linked to OA progression [33]. Varus thrust is a sudden lateral shift of the knee when the weight on the limb increases during the loading phase at the beginning of the gait cycle (frontal plane). It is well known that this mechanism is associated with pain and increase risk of OA progression [34; 35]. Its significant role was also highlighted in another study assessing the impact of knee kinematics on the response to exercises [17]. As shown in this study, patients with a higher varus thrust at baseline were more likely to respond to a physical exercise program. Furthermore, a lower KOOS\({}_{pain}\) at baseline (<62.5) appeared to be predictive of an improvement for male patients. This was also observed by [PERSON] et al. [16] who used a predictive model based on principal component analysis, as well as being reported in the hip OA literature [36]. Notably, the discriminant features retained in the male prediction model were related to frontal and sagittal plane kinematics, whereas features in the female prediction model were all related to tibial rotation. Figure 5: Decision trees for each of the prediction models (a) Males and (b) Females. All features (except for KOOS\({}_{pain}\) score) are expressed in degrees. Female prediction model: external(+)/internal(\(-\)) tibia rotation. These results reached a state-of-the-art accuracy and compared favorably with others that have considered the prediction of the impact of physical exercise. Indeed, the purpose of Reference [16] was to use pre-intervention gait kinematics and patient-reported outcome measures to predict post-intervention response to a 6-week hip strengthening exercise intervention in patients with mild-to-moderate knee OA. Using a discriminant analysis on a small dataset (39 patients), the classification accuracy was 85.4%. Being male is associated with higher odds of presenting with a varus thrust [37], which could help explain the important role of this feature played in the male prediction model. This study suggests that exercises efficiently reduce pain in patients with varus thrust, supporting their relevance as a conservative treatment especially in the presence of this mechanical biomarker.