Robust Bidirectional Long Short-Term Memory-Based Class Imbalance Handling in Dyslexia Prediction at its Early Stage
Bidirectional long short-term memory; Class imbalance; Deep learning; Dyslexia; Hyperparameter; Machine learning; Neurological disorder; Red Fox optimization
Dyslexia is a neurological condition that presents difficulties and obstacles in learning, particularly in reading. Early diagnosis of dyslexia is crucial for children, as it allows the implementation of appropriate resources and specialized software to enhance their skills. However, the evaluation process can be expensive, time-consuming, and emotionally challenging. In recent years, researchers have turned to machine learning and deep learning techniques to detect dyslexia using datasets obtained from educational and healthcare institutions. Despite the existence of several deep learning models for dyslexia prediction, the problem of handling class imbalance significantly impacts the accuracy of detection. Therefore, this study proposes a robust deep learning model based on a variant of long short-term memory (LSTM) to address this issue. The advantage of Bidirectional LSTM, which has the ability to traverse both forward and backward, improves the pattern of understanding very effectively. Still, the problem of assigning values to the hyper-parameters in BLSTM is the toughest challenge which has to be assigned in a random manner. To overcome this difficulty, the proposed model induced a behavioral model known as Red Fox Optimization algorithm (RFO). Based on the inspiration of red fox searching behavior, this proposed work utilized the local and the global search in assigning and fine-tuning the values of hyper-parameters to handle the class imbalance in dyslexia dataset. The performance evaluation is conducted using two different dyslexia datasets (i.e., dyslexia 12_14 & real-time dataset). The simulation results explore that the proposed robust Bidirectional Long Short-Term Memory accomplishes the highest detection rate with reduced error rate compared to other deep learning models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Zeema J.L.; Thirunavukkarasu V.; Sivabalan R.V.; Christopher D.F.X.
Zeema J.L., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Thirunavukkarasu V., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Sivabalan R.V., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Christopher D.F.X., SRM Trichy Arts and Science College, Tamil Nadu, Tiruchirappalli, India