预测阿尔茨海默病的机器学习模型

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-03-20 DOI:10.1049/cps2.12090
Pooja Rani, Rohit Lamba, Ravi Kumar Sachdeva, Karan Kumar, Celestine Iwendi
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引用次数: 0

摘要

阿尔茨海默病(AD)是一种神经退行性疾病,主要影响老年人。其症状最初比较轻微,但随着时间的推移会越来越严重。虽然这种疾病无法治愈,但早期诊断有助于减少其影响。本文提出了一种用于预测阿尔茨海默病的方法 SMOTE-RF。使用机器学习算法预测阿尔茨海默氏症。评估了决策树、极梯度提升(XGB)和随机森林(RF)三种算法在预测中的表现。实验使用了 Kaggle 上的开放获取系列成像研究纵向数据集。该数据集使用合成少数超采样技术进行平衡。实验同时在不平衡和平衡数据集上进行。在不平衡数据集上,决策树获得了 73.38% 的准确率,XGB 获得了 83.88% 的准确率,RF 获得了最高 87.84% 的准确率。在平衡数据集上,决策树获得了 83.15% 的准确率,XGB 获得了 91.05% 的准确率,RF 获得了最高 95.03% 的准确率。SMOTE-RF 的最高准确率为 95.03%。
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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A machine learning model for Alzheimer's disease prediction

Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. A methodology SMOTE-RF is proposed for AD prediction. Alzheimer's is predicted using machine learning algorithms. Performances of three algorithms decision tree, extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies longitudinal dataset available on Kaggle is used for experiments. The dataset is balanced using synthetic minority oversampling technique. Experiments are done on both imbalanced and balanced datasets. Decision tree obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum of 87.84% accuracy on the imbalanced dataset. Decision tree obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. A maximum accuracy of 95.03% is achieved with SMOTE-RF.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
期刊最新文献
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