Early Stage Chronic Kidney Disease Prediction using Convolution Neural Network

N. Pareek, Deepika Soni, S. Degadwala
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Abstract

Significant numbers of individuals all around the globe are afflicted with chronic kidney disease (CKD). Preventing further problems and slowing the course of CKD requires early detection and treatment. To better detect early-stage CKD, this research suggests an AI-based smart expert system to analyze patient clinical data. The system makes predictions about CKD's early stages using a machine learning algorithm that takes as input data such as demographics, laboratory results, and clinical factors. Better patient outcomes and lower healthcare expenditures are two possible benefits of the suggested method to increase CKD diagnosis rates.
使用卷积神经网络预测早期慢性肾脏疾病
全球有相当数量的人患有慢性肾脏疾病(CKD)。预防进一步的问题和减缓CKD的进程需要早期发现和治疗。为了更好地检测早期CKD,本研究提出了一种基于人工智能的智能专家系统来分析患者的临床数据。该系统使用机器学习算法,将人口统计、实验室结果和临床因素等数据作为输入,对CKD的早期阶段进行预测。更好的患者预后和更低的医疗费用是建议的方法提高CKD诊断率的两个可能的好处。
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