A Constructive Model for Dyslexia and ADHD Prediction using Feature Learning and Boosting Approaches - Kothapalli, Pavan Kumar Varma; Rathikarani, V.; Nookala, Gopala Krishna Murthy (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Dyslexia and Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurological disorder characterized by speech and reading impairments. The disorder is commonly identified in school-aged people, most commonly in males and causes poor performance with low self-esteem. Based on the review, it is noted that there are various machine learning approaches are used for the prediction process, and the validation is done with the available dataset. However, the prediction process is complex in this cause due to the lack of a standard dataset, biologically-interpretable biomarkers, classifiers, under-fitting, over-fitting issues and so on. To successfully implement a better CDSS, some preliminary process needs to be done to enhance the prediction rate. It includes: data acquisition, pre-processing, and data augmentation process. Here, the available online dataset for dyslexia is occupied from the UCI Machine Learning source. Some appropriate features of dyslexia are acquired using Least Absolute Shrinkage and Selection Operator (LASSO) and Fisher-score Relief Model. A hybrid AdaBoosting is developed by integrating the conventional classifiers with the bagging and boosting model. The bagging and boosting process is considered during the training process. In this case, the model is simulated using the MATLAB 2020a simulation environment, and the performances are assessed to demonstrate the model’s importance. Metrics including accuracy, error rate, precision, recall, and F1-score are examined together with other statistical measurements. To further compare these findings with currently used methods, these results are supplied. This investigation demonstrates that the expected model achieves a better trade-off and a higher level of prediction accuracy.