Gait analysis plays a vital role in clinical assessments by providing clear, objective insights into how diseases progress, how impairments in walking are manifested, and the effectiveness of various treatments. Our study tackled the challenge of comparing individuals by using multiple linear regression models to account for personal physical differences. We also looked at improving these models by transforming variables. Our focus was on individuals with Parkinson’s disease, normal pressure hydrocephalus, and a control group without these conditions. We used statistical tests to select relevant features and reduced the number of variables using principal component analysis. Techniques like Random Forest, Support Vector Machine, and Multiple Linear Perceptron were used to understand the normalized data patterns. Additionally, SHapley’s Additive exPlanations method helped us identify which variables were most influential before and after data normalization. This work opens new possibilities for using these techniques in both clinical settings and further research.