## 1 Introduction
These systems vary in technology, using video recordings, 2D and 3D joint kinematics (with or without markers) and force plates to measure joint forces. Some devices can also assess dynamic muscle activity via electromyography and energy expenditure via oxygen consumption [17]. Despite advances in technology, objectively assessing gait pathology remains challenging. Therefore, various indices such as the Gillette Gait Index (GGI), Gait Deviation Index (GDI), Gait Profile Score (GPS) and Gait Variable Score (GVS) have been developed [18; 19; 20]. While the GDI and GPS are reliable for clinical use, qualitative tools such as GOAL and FAQ offer valuable subjective insights into gait function and its impact. Overall, these tools enhance the evaluation and improvement of gait function in children with CP [21; 22]. An important research direction in CP involves identifying correlations between health parameters and classification systems. [PERSON] et al. found a correlation between gross motor function and spatiotemporal gait parameters in children with CP or brain injuries [23]. [PERSON] et al. reported a significant relationship between the modified Gait Deviation Index(mGDI) and both GMFM-66 and GMFM-88 scores, with a stronger correlation to GMFM-66. They also showed that mGDI values vary by GMFCS level [24]. Another emerging research focus is the prediction of classification scores and health outcomes. [PERSON] et al. examined whether results from IGA using the Zebris FDM-T treadmill could predict GMFM-88 scores in individuals with CP. Their study analyzed 23 participants aged 8-24 years using the treadmill in standing and walking conditions. Generalized Linear Models (GLMs) were used to predict both total and subdomain GMFM-88 scores. The mean absolute error (MAE) for predicting the total GMFM-88 score was 5.4 points (range: 2.4-8-3; interquartile range: 1.5). The authors concluded that this approach could streamline motor function assessments, potentially facilitating clinical application [25]. [PERSON] et al. also explored machine learning for predicting GMFM scores. They developed a Medical Device Score Calculator (MDSC), which predicted GMFM-66 scores for 1639 patients with CP. The best-performing model, a random forest algorithm, achieved an MAE of 7.74 and a root mean square error (RMSE) of 10.1. The study showed that MDSC tended to overestimate scores at higher GMFCS levels and underestimate them at lower levels. Consequently, the authors recommend its use primarily for group comparisons in research rather than for individual assessments [26]. Machine learning is increasingly being applied in pediatric prediction models. Its applications include predicting functional, cognitive, and language development in children with developmental disorders [27] as well as predicting the progression of scoliosis curves in adolescents with idiopathic scoliosis (AIS) [28]. A recent study used machine learning to predict independent walking ability in children with CP. Logistic regression models showed high accuracy, and a clinically useful nomogram was developed [29]. The aim of this study is to investigate the accuracy of predicting GMFM-66 scores in ambulatory children and adolescents with CP using IGA results. Predicting GMFM-66 scores may support therapy evaluation within rehabilitation settings. Eliminating the need for full GMFM-66 testing, which typically takes 45 to 60 min, could save personnel resources. Given that the primary clinical benefit lies in time savings, this study focuses exclusively on pressure plate data, which requires the least time to collect.