基于机器学习算法的老年痴呆症分类框架

Pushkar Wankhede, Nandini Sakhare, Yogita K. Dubey, Manu Varghese, Shrutika Gawande, Manoj Patil
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引用次数: 0

摘要

全世界至少有5000万人被认为患有阿尔茨海默病(AD)。因此,早期诊断和早期剂量美金刚是抑制这种疾病进一步增加的必要条件。本文提出了一个使用五种不同的机器学习模型对阿尔茨海默氏症进行分类的框架。数据采集自Kaggle网站,数据预处理完成。五种不同的模型,即逻辑回归,支持向量机,随机森林,决策树和梯度增强分类器进行比较,使用各种评估指标,如准确性,精度,召回率,日志损失和AUC-ROC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Framework for Alzheimer Diseases Classification using Machine Learning Algorithms
At Least 50 million people in the world are assumed to have Alzheimer Disease (AD). Hence early diagnosis as well as early dose of memantine is required to inhibit further increase of this disease. In this paper a framework is proposed for classification of Alzheimer using five different Machine Learning models. Dataset is taken from Kaggle website after that Data preprocessing takes place. Five different models viz. Logistic Regression, Support Vector Machine, Random Forest, Decision Tree and Gradient Boosting Classifier are compared using various evaluation metrics such as Accuracy, Precision, Recall, Log loss and AUC-ROC.
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