干旱区生态环境知识资源中心(Arid): Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models

Arid

DOI	10.3389/fpubh.2022.853294
	Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models
	Kavitha, C.; Mani, Vinodhini; Srividhya, S. R.; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos Andres
通讯作者	Kavitha, C
来源期刊	FRONTIERS IN PUBLIC HEALTH
EISSN	2296-2565
出版年	2022
卷号	10
英文摘要	Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing at an alarming rate every year. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Open Access Series of Imaging Studies (OASIS) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML models. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with these ML algorithms. The proposed work shows better results with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.
英文关键词	healthcare prediction Alzheimer's disease (AD) machine learning feature selection
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000773147900001
WOS关键词	MODIFIABLE RISK-FACTORS ; DEMENTIA PREVENTION ; LIBRA ; LIFE
WOS类目	Public, Environmental & Occupational Health
WOS研究方向	Public, Environmental & Occupational Health
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392869
推荐引用方式 GB/T 7714	Kavitha, C.,Mani, Vinodhini,Srividhya, S. R.,et al. Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models[J],2022,10.
APA	Kavitha, C.,Mani, Vinodhini,Srividhya, S. R.,Khalaf, Osamah Ibrahim,&Tavera Romero, Carlos Andres.(2022).Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models.FRONTIERS IN PUBLIC HEALTH,10.
MLA	Kavitha, C.,et al."Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models".FRONTIERS IN PUBLIC HEALTH 10(2022).