Unleashing the Potential of Artificial Bee Colony Optimized RNN-Bi-LSTM for Autism Spectrum Disorder Diagnosis

Abstract

The diagnosis of Autism Spectrum Disorder (ASD) is a crucial, drawn-out, and sometimes subjective procedure that calls for a high level of knowledge. Automation of this diagnostic procedure appears to be possible because to recent developments in machine learning techniques. This paper presents a unique method for improving the performance of a Recurrent Neural Network with a Bidirectional Long Short-Term Memory (RNN-BiLSTM) model for ASD diagnosis by utilizing the power of Artificial Bee Colony (ABC) optimization. Because Python software is used to carry out the implementation, accessibility and adaptability in clinical contexts are guaranteed. The suggested approach is thoroughly contrasted with current techniques, such as ABC optimization for feature extraction, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models, and Transfer Learning, in order to highlight its effectiveness. The outcomes demonstrate the superiority of the RNN-BiLSTM over other methods, with much greater accuracy and precision. Combining RNN-BiLSTM with ABC optimization demonstrates not just cutting-edge accuracy but also excellent interpretability. By using this sophisticated model's capabilities, an outstanding diagnosis accuracy of 99.12% is attained, which is 2.77% higher than previous approaches. The model helps physicians comprehend the diagnosis process by highlighting important characteristics and trends that influence its conclusion. Additionally, it lessens the subjectivity and unpredictability involved in human diagnosis, which may result in quicker and more accurate diagnoses of ASD. The research emphasizes how well the Artificial Bee Colony optimized RNN-BiLSTM model diagnoses autism spectrum disorder. By integrating AI-driven diagnostic tools into clinical practice, this research improves early diagnosis and intervention for ASD.