基于MLP神经网络的尿液指标分析模型

Urine Parameter Analysis Model Based on MLP Neural Network

摘要: 本研究提出了一种基于多层感知器(MLP)神经网络的尿液指标分析模型，旨在识别和预测尿液11项指标状态。尿液作为一种便捷且非侵入性的生物样本，具有重要的临床诊断价值，我们使用大量的尿液试剂条样本对MLP神经网络模型进行训练和测试，以提取尿液中的重要特征并进行分类预测。实验结果显示，模型的精确率、召回率和F1值等性能指标都达到了90%以上，证明了模型在识别和预测尿液11项指标方面表现优异，为临床诊断提供了一种快速、准确且成本效益高的工具。本文的研究成果为尿液检测技术的发展提供了新思路，并展示了机器学习在医疗诊断中的广泛应用潜力。

Abstract: This study proposes a urine parameter analysis model based on a multilayer perceptron (MLP) neural network, aimed at identifying and predicting the status of 11 urine parameters. Urine, as a convenient and non-invasive biological sample, holds significant clinical diagnostic value. We trained and tested the MLP neural network model using a large number of urine reagent strip samples to extract key features from the urine and perform classification predictions. Experimental results demonstrate that the model achieved performance metrics, including precision, recall, and F1 score, all exceeding 90%, confirming its excellent performance in identifying and predicting the 11 urine parameters. This model provides a rapid, accurate, and cost-effective tool for clinical diagnosis. The findings of this study offer new insights into the development of urine testing technologies and highlight the broad potential applications of machine learning in medical diagnostics.