Machine Learning Techniques to Enhance the Mental Age of Down Syndrome Individuals: A Detailed Review

Down syndrome individuals are known as intellectually disabled people. Their intellectual ability is classified into four categories known as mild, moderate, severe, and profound. These individuals have significant limitations in learning and adapting skills. Psychologists evaluate mental capability of such individuals using conventional intellectual quotient method instead of using any technology. The research matrix shows most of research has been carried out on analyzing neuroimaging, antenatal screening, and hearing impairment of individuals. But there is still an obvious gap of evaluating mental age using artificial intelligence. We have proposed an artificial neural network model, which supervises how software is used to obtain dataset using Knowledge Base Decision Support System. In a survey (N = 120) individuals examined by psychiatrist, medical expert, and a teacher to assess the presence of Down’s syndrome by analyzing their physical and facial appearances, and communication skills. Only (N = 62) individuals declared as Down syndrome. Selected individuals invited to perform mental ability assessment using Interactive Mental Learning Software. The results of mental age of Down syndrome with a raise in IQ from severe to moderate (20% to 35%), moderate to mild (35% to 75%) severity were carried out with the help of assessing the interactive series of software opinion polls based on comparison, logic, and basic mathematical operations using initial IQ (iIQ), and enhanced IQ (eIQ) variables input and output parameters.

@article{Leghari2023,
  title     = {Machine Learning Techniques to Enhance the Mental Age of Down Syndrome Individuals: A Detailed Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {1},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Irfan M. Leghari and Hamimah Ujir and SA Ali and Irwandi Hipiny},
  doi       = {10.14569/IJACSA.2023.01401107},
  url       = {https://doi.org/10.14569/IJACSA.2023.01401107}
}