<![CDATA[ASD is a neurodevelopmental disorder that affects a child's ability to manage emotions, interact socially, and respond to the environment. The main challenge in monitoring children's physiological condition is the limited availability of objective observation methods that rely heavily on health professionals. One potential objective approach is to analyze the ECG signal. However, ECG signals in children with ASD generally have high levels of noise due to body movements during recording, making manual analysis and conventional methods difficult. This study aims to develop a classification system for the physiological condition of children with ASD based on ECG signals, specifically to distinguish between quiet and active states. The dataset consists of 1000 from each of the two active classes and 1000 from the quiet class. ECG signals were processed using DWT for filtering, and then classified using three machine learning algorithms: SVM, RF, and AdaBoost. The performance of each model was evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that Random Forest provided the best performance, with an accuracy value of 93%. Meanwhile, SVM achieved an accuracy of 91.25%, while AdaBoost showed slightly lower performance at 90.00%. Based on these results, Random Forest was selected as the most optimal model and integrated into a web-based system using Streamlit. This study demonstrates that the combination of DWT and Random Forest is effective for classifying the physiological conditions of autistic children and has the potential to serve as an objective tool for monitoring them.]]>
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