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基于多层感知子的自我护理对残疾儿童的早期预测

Rahman Ali¹, Jamil Hussain², Seung Won Lee³

¹Quaid-e-Azam College of Commerce, University of Peshawar, Khyber Pakhtunkhwa, Pakistan.

|July 10, 2023

概括

这项研究介绍了MLP-progressive,这是一种新的人工神经网络方法,用于早期检测儿童的自我护理障碍. 机器学习方法显著提高了诊断准确度,帮助治疗师更快地识别残疾.

科学领域:

儿科治疗治疗的方法
医疗保健中的机器学习
检测残疾人的残疾

背景情况:

由于复杂的评估过程,对儿童自我照顾障碍的早期识别具有挑战性.
机器学习为提高残疾检测效率和准确性提供了潜在的解决方案.

研究的目的:

提出和验证MLP-progressive,一种新的人工神经网络方法论,用于早期发现儿童自我照顾障碍.
通过先进的预处理技术,提高多层感知子 (MLP) 模型的性能.

主要方法:

开发了MLP渐进式,集成无监督的基于实例的重新采样和随机预处理与前人工神经网络.
验证了多类和二进制类数据集的方法.
评估模型性能使用准确度,精度,回忆,F-测量,TP率,FP率和ROC.

主要成果:

MLP-progressive实现了高分类准确度:97.14%的多类数据集和98.57%的二进制类数据集.
与最先进的方法相比,观察到显著的准确性改善,在多类数据集上从90.00%到97.14%不等.
提出的预处理技术明显改善了MLP模型的性能.

结论:

随着MLP的逐步方法提供了一个非常准确和高效的方法,用于早期发现儿童的自我照顾障碍.
这种机器学习模型的性能优于现有的方法,为治疗师提供了宝贵的工具.
在这个领域,预处理技术对于优化人工神经网络模型的性能至关重要.

关键词:

一个年龄,一个年龄.人工神经网络的人工神经网络在MLP中,MLP是MLP.孩子们的孩子们的孩子们的孩子们.身体残疾就是残疾.多层感知器多层感知器预测预测预测预测

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