Optimizing Interactive Mental Learning Activity Software for Accurate Cognitive Profiling in Individuals with Down Syndrome

Abstract

Down syndrome is a lifelong cognitive impairment characterized by lower mental skills and intelligence quotient (IQ) compared to their typical peers. The profile is not curable. However, research has been conducted to supplement and improve cognitive functioning through computing and software applications. Conventional cognitive applications and IQ scales pose significant challenges as they are not developed based on specific cognitive guidelines. Therefore, such methods often fail to accurately assess cognitive profiling, resulting in uncertainty. To overcome these challenges, Interactive Mental Learning Activity Software utilizes tailored guidelines incorporating fuzzy logic rules, ensuring accurate cognitive profiling for Down syndrome. Fuzziness was applied within the machine learning framework across three groups structured based on IQ levels. A total of N=200 individuals with Down syndrome participated in the IQ assessment. The findings revealed that individuals with mild impairment demonstrated a higher degree of improvement in cognitive abilities compared to moderate and severe levels. However, the severe category appears to have an unrealistic probability, leading to a standstill in progress. The implementation of the specific guided system led to improvements of 6%, 5%, and 5% in individuals with mild, moderate, and severe cases, respectively.