The proposed ADHD Detection System integrates eye pupil and electrocardiogram (ECG) analyses to enhance the accuracy of Attention-Deficit/Hyperactivity Disorder (ADHD) identification. Developed in response to the limitations of existing diagnostic methods, the system aims to provide a more objective and efficient approach to ADHD diagnosis. By leveraging a comprehensive dataset encompassing eye movement and ECG data. 

1- Develop web application designed for the detection of ADHD.

2- Provide a reliable and accurate method for diagnosing ADHD based on the analysis of ECG signals and eye-tracking data.

3- Implement a user interface design that prioritizes simplicity and ease of use.

4- The system will reduce the effort and time spent in ADHD detection.

The ADHD Detection System is a web-based application designed to assist healthcare professionals, patients, data scientists, and students in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). The system utilizes machine learning algorithms to analyze Electrocardiogram (ECG) signals and eye-tracking data to identify potential indicators of ADHD. The scope of the system encompasses user registration, data capture, preprocessing, analysis, and result presentation. It serves as a collaborative platform for healthcare professionals and patients to discuss findings and make informed decisions regarding ADHD diagnosis.