Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)

• URL: http://arxiv.org/abs/2311.04606v1
• Date: Wed, 8 Nov 2023 11:14:29 GMT
• Title: Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)
• Authors: Ali Mohammadifar, Hasan Samadbin, Arman Daliri
• Abstract summary: We have achieved 99% accuracy for predicting autism spectrum disorder and we have achieved 13% improvement in the results. In this article, we have achieved 99% accuracy for predicting autism spectrum disorder and we have achieved 13% improvement in the results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The path to an autism diagnosis can be long and difficult, and delays can have serious consequences. Artificial intelligence can completely change the way autism is diagnosed, especially when it comes to situations where it is difficult to see the first signs of the disease. AI-based diagnostic tools may help confirm a diagnosis or highlight the need for further testing by analyzing large volumes of data and uncovering patterns that may not be immediately apparent to human evaluators. After a successful and timely diagnosis, autism can be treated through artificial intelligence using various methods. In this article, by using four datasets and gathering them with the federated learning method and diagnosing them with the support vector classifier method, the early diagnosis of this disorder has been discussed. In this method, we have achieved 99% accuracy for predicting autism spectrum disorder and we have achieved 13% improvement in the results.

Related papers

• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis [84.6348989654916]
  We propose the first foundational framework for early and timely diagnosis. It builds on decision-theoretic approaches to outline the diagnosis process. It integrates machine learning and statistical methodology for estimating the optimal personalized diagnostic path.
  arXiv  Detail & Related papers  (2023-11-26T14:42:31Z)
• Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor [15.39635888144281]
  We resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance.
  arXiv  Detail & Related papers  (2023-07-12T22:08:22Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations [64.91773761529183]
  Primary open-angle glaucoma (POAG) is the leading cause of blindness in the United States. Deep learning has been widely used to detect POAG using fundus images. Human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models.
  arXiv  Detail & Related papers  (2023-01-26T18:53:09Z)
• Action-based Early Autism Diagnosis Using Contrastive Feature Learning [2.922007656878633]
  Autism, also known as Autism Spectrum Disorder (or ASD), is a neurological disorder. Its main symptoms include difficulty in (verbal and/or non-verbal) communication, and rigid/repetitive behavior. We present a learning based approach to automate autism diagnosis using simple and small action video clips of subjects.
  arXiv  Detail & Related papers  (2022-09-12T16:31:34Z)
• Activity Recognition for Autism Diagnosis [1.4854189993691178]
  A formal autism diagnosis is an inefficient and lengthy process. One approach to this problem is to use digital technologies to detect the presence of behaviors related to autism. One of the strongest indicators of autism is stimming, which is a set of repetitive, self-stimulatory behaviors.
  arXiv  Detail & Related papers  (2021-08-18T01:00:02Z)
• Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
  Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD. Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
  arXiv  Detail & Related papers  (2020-08-21T20:22:55Z)
• A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children [0.0]
  We propose a convolutional neural network (CNN) algorithm for gaze prediction using images extracted from a one-minute stimulus video. Our model achieved a high accuracy rate and robustness for prediction of gaze direction with independent persons.
  arXiv  Detail & Related papers  (2020-07-28T18:47:21Z)
• Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis [142.90770786804507]
  Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases. This work attempts to address these critical issues in MDA by taking advantage of the causal diagram. We propose a propensity-based patient simulator to effectively answer unrecorded inquiry by drawing knowledge from the other records.
  arXiv  Detail & Related papers  (2020-03-14T02:05:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.