Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease

Figure 4

Box-plot representation of raw versus adjusted and normalized data of MoCA, total distance covered, and range of sway on -axis in controls and Alzheimer’s disease (AD) groups. There were significant differences between the two groups in raw data (MoCA, ; distance covered, ; -axis range, ) and after data was adjusted for age, education, height, and weight, followed by a normalization process (MoCA, ; distance covered, , -axis range, ). Despite these statistical differences, especially in unadjusted raw data, it is important to note the significant overlap between the different individuals in the kinematic postural variables, highlighting the challenge of classification based on machine learning.