Enhancing autism severity prediction: A fusion of convolutional neural networks and random forest model

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
R. Ramya, S. Arokiaraj
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引用次数: 0

Abstract

A neurological condition affecting both the brain and behaviour is identified as autism spectrum disorder (ASD). Due to the absence of a reliable medical test for detecting autism, diagnoses rely on historical evidence. Essential in assessing the degree of autism are models like Convolutional Neural Networks (CNNs) and Random Forest (RF). In order to reduce the amount of diagnostic tests required for autism diagnosis, this research work presents a new hybrid model that combines the strengths of RF and CNNs, providing healthcare solutions. It is noteworthy that this model properly predicted the severity of autism with an astounding accuracy rate of 99.15% when applied to historical data gathered from the Kaggle Repository.
加强自闭症严重程度预测:卷积神经网络与随机森林模型的融合
自闭症谱系障碍(ASD)是一种影响大脑和行为的神经系统疾病。由于缺乏检测自闭症的可靠医学测试,诊断只能依靠历史证据。卷积神经网络(CNN)和随机森林(RF)等模型对评估自闭症的程度至关重要。为了减少自闭症诊断所需的诊断测试数量,这项研究工作提出了一种新的混合模型,该模型结合了 RF 和 CNN 的优势,为医疗保健提供了解决方案。值得注意的是,当该模型应用于从 Kaggle 存储库中收集的历史数据时,能正确预测自闭症的严重程度,准确率高达 99.15%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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