Alzheimer’s Disease Diagnosis using Deep Learning Techniques

Salehi, Ahmad Waleed; Baglat, Preety; Gupta, Gaurav

doi:10.35940/ijeat.c5345.029320

Cited by 28 publications

(5 citation statements)

References 13 publications

(17 reference statements)

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“…Implementation speci cs: The size of a Random Forest model can also be in uenced by the speci c implementation details and optimizations employed by the machine learning library or framework used. Table 2 shows the advantages and disadvantages of various algorithms used for Alzheimer's illness using ML and DL techniques [50]. Heart Rate Variability: It refers to the variation in time intervals between successive heartbeats.…”

Section: B Biomarkers For Alzheimer's Diseasesmentioning

confidence: 99%

Biomarkers Classification for Various Brain Disease using Artificial Intelligence Approach-A Study

Reeja¹,

Mounika²,

Mohanty³

2023

Preprint

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Diagnostic and prognostic markers for disease identification Furthermore impact of treatment on the illness continues to be a significant restriction in science. As a matter of fact, initial determination and guess of the sickness are hindered by a lack of suitable markers, and as a result, many lives are lost due to a variety of diseases because diagnosis occurs too late for a severe form of the illness because it is challenging to comprehend a recurrence. An illness’s medical condition can be positively identified using biomarkers. Diagnostic biomarkers may be crucial in this situation to make a precise diagnosis, identify individuals with a disease, and classify people with the same type of condition to tailor pharmacological therapies and boost therapeutic response. As a result, these biomarkers may be helpful for more than only diagnosing diseases; they may also be able to anticipate how a patient will respond to treatment. Research in the biology of disease is therefore heavily focused on finding appropriate and useful biomarkers for disease. Finding disease-specific biomarkers has recently been aided by deep learning. Brain functional connectivity (FC) changes may serve as biomarkers for forecasting several types of brain diseases. When the alterations are modest and there aren't any major structural changes overall, fMRI may be able to find abnormalities in the brain that other imaging methods can't. FMRI analytics are frequently used in various brain investigations, even clinical trials, in conjunction with deep learning models.

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Section: B Biomarkers For Alzheimer's Diseasesmentioning

confidence: 99%

Biomarkers Classification for Various Brain Disease using Artificial Intelligence Approach-A Study

Reeja¹,

Mounika²,

Mohanty³

2023

Preprint

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“…In recent years, there has been a trend toward machine learning algorithms in different fields such as medical diagnosis [6,7], earthquake prediction [8], churn prediction [9], voice recognition [10], scientific research [11,12] and risk estimation [13]. The purpose of using these algorithms is to avoid human interference and minimize the error rate.…”

Section: Related Workmentioning

confidence: 99%

WisdomModel: convert data into wisdom

Mahmood

Abdullah

2021

ACI

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PurposeTraditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.Design/methodology/approachThe proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.FindingsThe authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.Originality/valueThis research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

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“…The use of wearable devices has been adopted by many people, but we still need to involve IoT with more advanced AI-based techniques [ 8 ] for improving the quality of people with Alzheimer's [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Wearable Devices for Patients Suffering from Alzheimer Disease

Salehi

Gupta

Bhatia

et al. 2022

Contrast Media & Molecular Imaging

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The disorder of Alzheimer’s (AD) is defined as a gradual deterioration of cognitive functions, such as the failure of spatial cognition and short-term memory. Besides difficulties in memory, a person with this disease encounters visual processing difficulties and even awareness and identifying of their beloved ones. Nowadays, recent technologies made this possible to connect everything that exists around us on Earth through the Internet, this is what the Internet of Things (IoT) made possible which can capture and save a massive amount of data that are considered very important and useful information which then can be valuable in training of the various state-of-the-art machine and deep learning algorithms. Assistive mobile health applications and IoT-based wearable devices are helping and supporting the ongoing health screening of a patient with AD. In the early stages of AD, the wearable devices and IoT approach aim to keep AD patients mentally active in all of life’s daily activities, independent from their caregivers or any family member of the patient. These technological solutions have great potential in improving the quality of life of an AD patient as this helps to reduce pressure on healthcare and to minimize the operational cost. The purpose of this study is to explore the State-of-the-Art wearable technologies for people with AD. Significance, challenges, and limitations that arise and what will be the future of these technological solutions and their acceptance. Therefore, this study also provides the challenges and gaps in the current literature review and future directions for other researchers working in the area of developing wearable devices.

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Alzheimer’s Disease Diagnosis using Deep Learning Techniques

Cited by 28 publications

References 13 publications

Biomarkers Classification for Various Brain Disease using Artificial Intelligence Approach-A Study

Biomarkers Classification for Various Brain Disease using Artificial Intelligence Approach-A Study

WisdomModel: convert data into wisdom

IoT-Based Wearable Devices for Patients Suffering from Alzheimer Disease

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