ASSESSING THE COMBINED DRUG EFFICACY FOR ADHD TREATMENT IN ADULTS AND CHILDREN USING A NOVEL BIG DATA ANALYTICAL MODEL: A DOSAGE-DEPENDENT STUDY

Abstract

A common neurodevelopment condition known as attention deficit hyperactivity disorder (ADHD) is defined by age-inappropriate symptoms of inattention and hyperactivity that continue into adulthood in the majority of children who are diagnosed with the condition as a child. The purpose of this study was to find out how combo medications work for treating ADHD patients. Both children and adults were included in the research. For this investigation, a total of 1500 patient records were collected. The Map Reduce method was used to eliminate duplicate data missing values and to create effective data partitioning. Recursive Feature Elimination (RFE) was employed to remove less significant features from a dataset and improve machine learning model performance. To find the efficacy of the drug used to treat ADHD Brain disorder in adults & children in this study, we proposed a time-dependent flexible deep recurrent network (TDF-DRN) technique. According to preliminary research, combining medication therapy can be a successful strategy for treating the symptoms of ADHD in both adults and children. The best medicine combinations and dosages seem to differ depending on the age group. Compared to adults, children need fewer dosages and benefit from various combinations. Additionally, the safety concerns and potential adverse effects of different drug combinations are assessed.