干旱区生态环境知识资源中心(Arid): Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset

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DOI	10.1109/ACCESS.2021.3066213
	Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset
	Basheer, Shakila; Bhatia, Surbhi; Sakri, Sapiah Binti
通讯作者	Bhatia, S (corresponding author), King Faisal Univ, Coll Comp Sci & Informat Technol, Dept Informat Syst, Al Hufuf 36363, Saudi Arabia.
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2021
卷号	9 页码:42449-42462
英文摘要	Alzheimer is a progressive disease and it is the most prevalent neurodegenerative disorder. It is believed that the people with mild cognitive impairment are at high risk of developing this disease. According to the annual report released by the Alzheimer's Association (R) 2020, Alzheimer is the sixth leading cause of death in the United States. Thus, there is a need of educating people about this disease, reducing the risks by militating the necessary precautions to disseminate its affect by diagnosing it at early stages. It is also important to propose some recent advancement in this research which can help in early prediction of the disease using machine learning techniques. This paper intends to develop the novel algorithm by proposing changes in the designing of capsule network for best prediction results and making the model computationally efficient. The research is conducted on the Open Access Series of Imaging Studies (OASIS) dataset with dimensions (373 X 15) to diagnose the labels into two groups, as demented and non-demented. The novelty lies in conducting the in-depth research in identifying the importance of features, correlation study between factors and density of data showing status of factors by studying hierarchical examination of all the data points available using exploratory data analysis. Several optimization functions are conducted on the variables and feature selection is done to make the model faster and more accurate. The claims have been validated by showing the correlation accuracy at several iterations and layers with an admissible accuracy of 92.39%. The model is compared with state-of-art deep learning classifiers taken as benchmarks using different performance metrics. The ablation study is conducted on the proposed model using OASIS dataset to justify the predictions of the model.
英文关键词	Feature extraction Deep learning Magnetic resonance imaging Machine learning Predictive models Neuroimaging Prediction algorithms Dementia Alzheimer’ s disease neural network models machine learning deep learning convolutional neural networks capsule networks
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000633619000001
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350526
作者单位	[Basheer, Shakila; Sakri, Sapiah Binti] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, Riyadh 11671, Saudi Arabia; [Bhatia, Surbhi] King Faisal Univ, Coll Comp Sci & Informat Technol, Dept Informat Syst, Al Hufuf 36363, Saudi Arabia
推荐引用方式 GB/T 7714	Basheer, Shakila,Bhatia, Surbhi,Sakri, Sapiah Binti. Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset[J],2021,9:42449-42462.
APA	Basheer, Shakila,Bhatia, Surbhi,&Sakri, Sapiah Binti.(2021).Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset.IEEE ACCESS,9,42449-42462.
MLA	Basheer, Shakila,et al."Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset".IEEE ACCESS 9(2021):42449-42462.