{"title":"Machine and deep learning algorithms for classifying different types of dementia: A literature review.","authors":"Masoud Noroozi, Mohammadreza Gholami, Hamidreza Sadeghsalehi, Saleh Behzadi, Adrina Habibzadeh, Gisou Erabi, Sayedeh-Fatemeh Sadatmadani, Mitra Diyanati, Aryan Rezaee, Maryam Dianati, Pegah Rasoulian, Yashar Khani Siyah Rood, Fatemeh Ilati, Seyed Morteza Hadavi, Fariba Arbab Mojeni, Minoo Roostaie, Niloofar Deravi","doi":"10.1080/23279095.2024.2382823","DOIUrl":null,"url":null,"abstract":"<p><p>The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.</p>","PeriodicalId":51308,"journal":{"name":"Applied Neuropsychology-Adult","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Neuropsychology-Adult","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/23279095.2024.2382823","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.
被称为痴呆症的认知障碍影响着全球数百万人。机器学习(ML)和深度学习(DL)算法的使用为痴呆症的早期识别和治疗带来了巨大希望。本文将讨论阿尔茨海默氏症、额颞叶痴呆症、路易体痴呆症和血管性痴呆症等痴呆症,以及在诊断中使用 ML 算法的文献综述。文章对支持向量机、人工神经网络、决策树和随机森林等不同的 ML 算法及其优点和缺点进行了比较和对比。正如本文所讨论的,通过仔细考虑特征选择和数据准备,可以建立准确的 ML 模型。我们还讨论了 ML 算法如何预测疾病进展和患者对治疗的反应。但是,在没有进一步证明的情况下,应避免过度依赖 ML 和 DL 技术。需要注意的是,这些技术旨在辅助诊断,但不应作为最终诊断的唯一标准。研究表明,ML 算法可能有助于提高痴呆症诊断的准确性,尤其是在痴呆症的早期阶段。ML和DL算法在临床环境中的有效性必须得到验证,使用个人数据的伦理问题也必须得到解决,但这需要更多的研究。
期刊介绍:
pplied Neuropsychology-Adult publishes clinical neuropsychological articles concerning assessment, brain functioning and neuroimaging, neuropsychological treatment, and rehabilitation in adults. Full-length articles and brief communications are included. Case studies of adult patients carefully assessing the nature, course, or treatment of clinical neuropsychological dysfunctions in the context of scientific literature, are suitable. Review manuscripts addressing critical issues are encouraged. Preference is given to papers of clinical relevance to others in the field. All submitted manuscripts are subject to initial appraisal by the Editor-in-Chief, and, if found suitable for further considerations are peer reviewed by independent, anonymous expert referees. All peer review is single-blind and submission is online via ScholarOne Manuscripts.