Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease

Thapa, Surendrabikram; Singh, Priyanka; Jain, Deepak Kumar; Bharill, Neha; Gupta, Akanksha; Prasad, Mukesh

doi:10.1109/ijcnn48605.2020.9207359

Cited by 39 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, it uses support vector machine to classify data. Thapa et al ( 2020 ) proposed a data-driven technology based on feature selection for early diagnosis of Alzheimer's disease. They pointed out that the combination of neuropsychological scores and MRI features could be helpful for the early diagnosis of Alzheimer's disease.…”

Section: Related Workmentioning

confidence: 99%

Multi-modal feature selection with anchor graph for Alzheimer's disease

Xu²,

Yu³

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-modal feature selection with anchor graph for Alzheimer's disease

Xu²,

Yu³

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…With the rapid development of Artificial Intelligence, the healthcare industry has been completely improved [3]. Various state-of-the-art technologies such as Artificial Neural Networks, Convolutional Neural Networks, and others are being used in various health care fields such as ophthalmology, radiology, dermatology, and genome interpretation [4]. In addition, In the healthcare sector, the Internet of Things (IoT) has revolutionized sensors to monitor a patient's physiological system [5].…”

Section: Applications Of Ai and Iot In Covid-19mentioning

confidence: 99%

Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life

Almars

Gad

Atlam

2021

Studies in Computational Intelligence

View full text Add to dashboard Cite

COVID-19 illness has been recognized as an International Public Health Emergency (PHEIC). According to the World Health Organization (WHO), more than 200 countries worldwide have been impacted by the COVID-19. More than 4.7 M people in the globe have lost their lives due to the coronavirus pandemic. Governments and hospitals have taken several actions and measures to prevent the further spread of infectious diseases, protect all people, and minimize illness and death rates. Artificial Intelligence (AI) and the Internet of Things (IoT) have great potential to create prevailing tools for battling COVID-19. Several technologies such as deep learning, machine learning, natural language processing, and the Internet of Things have been used to properly address healthcare concerns, including diagnosis, drug and vaccine development, vaccine distribution, sentiment analysis, and fake news identification regarding COVID-19 reviews. The primary goal of this chapter is to focus on the potential of Artificial Intelligence (AI) and the Internet of Things (IoT). This chapter aims to provide a comprehensive overview of different approaches of AI and IoT related to combating COVID-19. In addition, the chapter discusses several actions and measurements that governments have used to prevent the spread of COVID-19. This chapter covers all methodologies applied to forecast the acceptance and the demand of the COVID-19 vaccine.

show abstract

“…Multiple theories have been suggested to address the etiology of Alzheimer’s disease, such as genetic abnormalities, oxidative stress ( Decourt et al, 2021 ), β-amyloid toxicity, and environmental factors ( Salahuddin et al, 2021 ). Alzheimer’s disease or an associated type of dementia affects roughly 50 million individuals worldwide, with that figure estimated to rise to 152 million by 2050 ( Thapa et al, 2020 ). Alzheimer’s disease affects more than 5.8 million individuals in the United States, with 13.8 million Americans over 65 estimated to be affected by the disease by 2050 ( Calderón-Garcidueñas et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Role of Environmental Toxicants on Neurodegenerative Disorders

Nabi

Tabassum

2022

Front. Toxicol.

View full text Add to dashboard Cite

Neurodegeneration leads to the loss of structural and functioning components of neurons over time. Various studies have related neurodegeneration to a number of degenerative disorders. Neurological repercussions of neurodegeneration can have severe impacts on the physical and mental health of patients. In the recent past, various neurodegenerative ailments such as Alzheimer’s and Parkinson’s illnesses have received global consideration owing to their global occurrence. Environmental attributes have been regarded as the main contributors to neural dysfunction-related disorders. The majority of neurological diseases are mainly related to prenatal and postnatal exposure to industrially produced environmental toxins. Some neurotoxic metals, like lead (Pb), aluminium (Al), Mercury (Hg), manganese (Mn), cadmium (Cd), and arsenic (As), and also pesticides and metal-based nanoparticles, have been implicated in Parkinson’s and Alzheimer’s disease. The contaminants are known for their ability to produce senile or amyloid plaques and neurofibrillary tangles (NFTs), which are the key features of these neurological dysfunctions. Besides, solvent exposure is also a significant contributor to neurological diseases. This study recapitulates the role of environmental neurotoxins on neurodegeneration with special emphasis on major neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease.

show abstract

Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease

Cited by 39 publications

References 21 publications

Multi-modal feature selection with anchor graph for Alzheimer's disease

Multi-modal feature selection with anchor graph for Alzheimer's disease

Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life

Role of Environmental Toxicants on Neurodegenerative Disorders

Contact Info

Product

Resources

About