Predicting Optimal Drug Dosage and Half-life Period of the Drug for the Treatment of Attention Deficit/Hyperactivity Disorder (ADHD) in Both Adults and Children Using a Novel Big Data Model Deep Convolve NeuroNet Algorithm.

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a common condition that affects both children and adults. It's important to find the right medication dose for each individual, taking into account factors like age, gender, and how the body responds to the medication. In this article, we suggest a detailed strategy for determining the best medication doses for treating ADHD using an advanced technology called Deep Convolve NeuroNet (DCNN). Our database includes records of people with ADHD, along with details about their age, gender, and how they responded to medication. By using a method called Map-Reduce, we can organize the information in the databases. After preparing the data, we use a method called linear discriminant analysis (LDA) to find important information, reduce complexity, and help with classification. One of the challenges in choosing the right medication dose is identifying the most important factors. Our approach is more effective because we use a method called Correlation-based Feature Selection (CFS), which helps us select the most useful information. We use a DCNN model that has been trained to predict the best medication dose for each patient. This advanced method allows us to handle complicated data relationships, providing accurate and personalized dose recommendations. Our goal is to give healthcare professionals a valuable tool for customizing medication doses for individual patients, improving treatment effectiveness, and minimizing side effects.