使用机器学习分类器和数据科学的健康记录预测糖尿病视网膜病变

Q2 Nursing
B. Sumathy
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引用次数: 5

摘要

糖尿病是一种迅速蔓延的疾病。当胰腺产生的胰岛素不足或身体不能有效地利用它。糖尿病视网膜病变(DR)和失明是糖尿病患者的两大问题。为了从数据中提取重要信息和未被发现的知识,需要使用数据挖掘技术。糖尿病是DR中改善社会健康所必需的。我们的研究重点是利用患者信息进行糖尿病视网膜病变的早期检测。DM方法用于从这些数字记录中提取信息。该数据集使用逻辑回归、KNN、SVM、袋装树和提升树分类器来预测DR。使用两次交叉验证来找到最佳特征并避免过拟合。我们的数据集包括900名糖尿病患者。增强树在10%的保留验证下产生了最佳的分类精度(90.1%)。KNN也达到了88.9%的准确率,这令人印象深刻。因此,我们的研究表明,袋装树和KNN是很好的DR分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Diabetic Retinopathy Using Health Records with Machine Learning Classifiers and Data Science
Diabetes is a rapidly spreading disease. When the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic Retinopathy (DR) and blindness are two major issues for diabetics. Diabetes patients increase the amount of data collected about DR. To extract important information and undiscovered knowledge from data, data mining techniques are required. DM is necessary in DR to improve society's health. Our study focuses on the early detection of Diabetic Retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. Our dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, our research suggests that bagged trees and KNN are good classifiers for DR.
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CiteScore
3.20
自引率
0.00%
发文量
43
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