机器学习算法在痴呆进展检测中的性能比较

Tripti Tripathi, R. Kumar
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引用次数: 0

摘要

痴呆症是一种神经系统疾病,包括多种情况,如语言交流、解决问题和其他判断能力,严重影响日常生活。它是全世界老年人易受伤害的主要原因之一。在这方面已经进行了大量的研究,以便我们能够早期发现这种疾病,但进一步研究改善这种疾病仍是一个新兴趋势。本文比较了使用脑MRI数据进行痴呆检测和分类的多种机器学习模型的性能,包括支持向量机、随机森林、AdaBoost和XGBoost。同时,利用ML算法和神经影像学数据对痴呆临床分类的论文进行系统评估。作者使用了来自OASIS数据库的373名参与者。其中,RF模型的准确率为83.92%,精密度为87.5%,召回率为81.67%,f1评分为84.48%,灵敏度为81.67%,特异性为88.46%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Comparison of Machine Learning Algorithms for Dementia Progression Detection
Dementia is a neurological disease that that encompasses a wide range of conditions like verbal communication, problem-solving, and other judgment abilities that are severely sufficient to interfere with daily life. It is among the leading causes of vulnerability among the elderly all over the world. A considerable amount of research has been conducted in this area so that we can perform early detection of the disease, yet further research into its betterment is still an emerging trend. This article compares the performance of multiple machine learning models for dementia detection and classification using brain MRI data, including support vector machine, random forest, AdaBoost, and XGBoost. Meanwhile, the research conducts a systematic assessment of papers for the clinical categorization of dementia using ML algorithms and neuroimaging data. The authors used 373 participants from the OASIS database. Among the tested models, RF model exhibited the best performance with 83.92% accuracy, 87.5% precision, 81.67% recall, 84.48% F1-score, 81.67% sensitivity, and 88.46% specificity.
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