Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

Abstract

Several variables, for instance, inheritance and surroundings, influence the growth of neurodevelopmental disorders, e.g., autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) during the first 36 months of life. ADHD and ASD diagnosis mainly rely heavily on traditional clinical assessments from the last few decades. These traditional methods are based on massive data collection from multiple respondents’ responses and the extent of various behavioral descriptors, which are then recognized by the researcher while forming a diagnostic criterion. However, opting for traditional diagnostic methods, there is a high possibility of being misdiagnosed, which may lead to the administration of unnecessary long-term pharmaceutical treatment. That may lead to reduction in functioning and an increase in the risk of developing additional social and clinical issues. Moreover, such diagnostic procedures are also time-consuming and costly. In this sense, rapid and advanced criteria are required to be accurate and cost-effective. Consequently, this study emphasizes the application of machine learning (ML) tools and deep learning (DL) techniques such as convolutional neural network (CNN) and Deep Learning APIs (Application Programming Interface), for the early diagnosis and treatment of ADHD and ASD symptoms. From this investigation, it can be concluded that diagnostic techniques based on ML reduce the intervention time and increase the accuracy with simultaneous understanding of the techniques and algorithms applied to different types of Image data. Numerous studies have been done on ASD and ADHD separately, but our investigation also focuses on cooccurrences of these disorders in one individual.

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

“…Motivated by the above analysis, the main goal of our work was to explore the newly proposed adversarial learning networks for generating data to solve the data imbalance problem. In practice, some challenges still exist, including (1) validating the similarity between the generated data and the original dataset, (2) generating the augmented data from a mixed data distribution using a single GAN model, and (3) performing a deterministic inverse transformation of the augmented data. The main objective of our work was not to achieve a high accuracy but to see whether we could replace the traditional data augmentation techniques with new techniques.…”

“…Any artificial intelligence application is mainly dependent on data [1]. Due to its numerous uses, AI has been incorporated in many areas such as healthcare [2][3][4][5], agriculture [6,7], multi-class image classification [8], image caption prediction [9], fake image identification [10], and other purposes [11][12][13]. In the majority of real-world classification applications, the training data shows a distribution with a long tail.…”

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

“…The adoption of artificial intelligence (AI) and deep learning [24,25] has resulted in significant advancements in the accuracy and efficiency of skin cancer classification, assisting in the disease's early diagnosis and treatment [26]. When trained on massive datasets of skin scans, AI systems may learn to recognize the characteristics of malignant cells and distinguish them from benign cells with high accuracy.…”

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

“…Machine learning (ML) is a subset of AI and a rapidly evolving field of study that aims to establish high-quality prediction models using search strategies, deep learning, and computational analysis to enable machines to learn to make autonomous decisions and improve their performance at specific tasks [42]. There are several uses for ML in health and healthcare [12,[43][44][45][46][47][48].…”

“…New prospects are presented to assist clinical decision-making through the use of AI algorithms, automated instruments for measuring, decision-making, and classification in communication deficiencies and NDs in the research setting [11][12][13][14][15]62]. Traditional ML approaches use separate feature extraction procedures and classification methods, but with Deep Learning these two procedures are done comprehensively [42]. For the ASD diagnosis in young children from 5 to 10 years old, an intelligent model has been presented based on resting-state functional magnetic resonance imaging data from global Autism Brain Imaging Data Exchange I and II datasets and using convolutional neural networks (CNNs) [63].…”