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      Development of a robust parallel and multi-composite machine learning model for improved diagnosis of Alzheimer's disease: correlation with dementia-associated drug usage and AT(N) protein biomarkers

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          Abstract

          Introduction

          Machine learning (ML) algorithms and statistical modeling offer a potential solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging multiple data sources and combining information on neuropsychological, genetic, and biomarker indicators. Among others, statistical models are a promising tool to enhance the clinical detection of early AD. In the present study, early AD was diagnosed by taking into account characteristics related to whether or not a patient was taking specific drugs and a significant protein as a predictor of Amyloid-Beta (Aβ), tau, and ptau [AT(N)] levels among participants.

          Methods

          In this study, the optimization of predictive models for the diagnosis of AD pathologies was carried out using a set of baseline features. The model performance was improved by incorporating additional variables associated with patient drugs and protein biomarkers into the model. The diagnostic group consisted of five categories (cognitively normal, significant subjective memory concern, early mildly cognitively impaired, late mildly cognitively impaired, and AD), resulting in a multinomial classification challenge. In particular, we examined the relationship between AD diagnosis and the use of various drugs (calcium and vitamin D supplements, blood-thinning drugs, cholesterol-lowering drugs, and cognitive drugs). We propose a hybrid-clinical model that runs multiple ML models in parallel and then takes the majority's votes, enhancing the accuracy. We also assessed the significance of three cerebrospinal fluid biomarkers, Aβ, tau, and ptau in the diagnosis of AD. We proposed that a hybrid-clinical model be used to simulate the MRI-based data, with five diagnostic groups of individuals, with further refinement that includes preclinical characteristics of the disorder. The proposed design builds a Meta-Model for four different sets of criteria. The set criteria are as follows: to diagnose from baseline features, baseline and drug features, baseline and protein features, and baseline, drug and protein features.

          Results

          We were able to attain a maximum accuracy of 97.60% for baseline and protein data. We observed that the constructed model functioned effectively when all five drugs were included and when any single drug was used to diagnose the response variable. Interestingly, the constructed Meta-Model worked well when all three protein biomarkers were included, as well as when a single protein biomarker was utilized to diagnose the response variable.

          Discussion

          It is noteworthy that we aimed to construct a pipeline design that incorporates comprehensive methodologies to detect Alzheimer's over wide-ranging input values and variables in the current study. Thus, the model that we developed could be used by clinicians and medical experts to advance Alzheimer's diagnosis and as a starting point for future research into AD and other neurodegenerative syndromes.

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          Most cited references88

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          Artificial intelligence in healthcare: past, present and future

          Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.
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            Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria.

            In the past 8 years, both the International Working Group (IWG) and the US National Institute on Aging-Alzheimer's Association have contributed criteria for the diagnosis of Alzheimer's disease (AD) that better define clinical phenotypes and integrate biomarkers into the diagnostic process, covering the full staging of the disease. This Position Paper considers the strengths and limitations of the IWG research diagnostic criteria and proposes advances to improve the diagnostic framework. On the basis of these refinements, the diagnosis of AD can be simplified, requiring the presence of an appropriate clinical AD phenotype (typical or atypical) and a pathophysiological biomarker consistent with the presence of Alzheimer's pathology. We propose that downstream topographical biomarkers of the disease, such as volumetric MRI and fluorodeoxyglucose PET, might better serve in the measurement and monitoring of the course of disease. This paper also elaborates on the specific diagnostic criteria for atypical forms of AD, for mixed AD, and for the preclinical states of AD. Copyright © 2014 Elsevier Ltd. All rights reserved.
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              2023 Alzheimer's disease facts and figures

              (2023)
              This article describes the public health impact of Alzheimer's disease, including prevalence and incidence, mortality and morbidity, use and costs of care, and the overall impact on family caregivers, the dementia workforce and society. The Special Report examines the patient journey from awareness of cognitive changes to potential treatment with drugs that change the underlying biology of Alzheimer's. An estimated 6.7 million Americans age 65 and older are living with Alzheimer's dementia today. This number could grow to 13.8 million by 2060 barring the development of medical breakthroughs to prevent, slow or cure AD. Official death certificates recorded 121,499 deaths from AD in 2019, and Alzheimer's disease was officially listed as the sixth-leading cause of death in the United States. In 2020 and 2021, when COVID-19 entered the ranks of the top ten causes of death, Alzheimer's was the seventh-leading cause of death. Alzheimer's remains the fifth-leading cause of death among Americans age 65 and older. Between 2000 and 2019, deaths from stroke, heart disease and HIV decreased, whereas reported deaths from AD increased more than 145%. This trajectory of deaths from AD was likely exacerbated by the COVID-19 pandemic in 2020 and 2021. More than 11 million family members and other unpaid caregivers provided an estimated 18 billion hours of care to people with Alzheimer's or other dementias in 2022. These figures reflect a decline in the number of caregivers compared with a decade earlier, as well as an increase in the amount of care provided by each remaining caregiver. Unpaid dementia caregiving was valued at $339.5 billion in 2022. Its costs, however, extend to family caregivers' increased risk for emotional distress and negative mental and physical health outcomes - costs that have been aggravated by COVID-19. Members of the paid health care workforce are involved in diagnosing, treating and caring for people with dementia. In recent years, however, a shortage of such workers has developed in the United States. This shortage - brought about, in part, by COVID-19 - has occurred at a time when more members of the dementia care workforce are needed. Therefore, programs will be needed to attract workers and better train health care teams. Average per-person Medicare payments for services to beneficiaries age 65 and older with AD or other dementias are almost three times as great as payments for beneficiaries without these conditions, and Medicaid payments are more than 22 times as great. Total payments in 2023 for health care, long-term care and hospice services for people age 65 and older with dementia are estimated to be $345 billion. The Special Report examines whether there will be sufficient numbers of physician specialists to provide Alzheimer's care and treatment now that two drugs are available that change the underlying biology of Alzheimer's disease.
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                Author and article information

                Contributors
                URI : http://loop.frontiersin.org/people/2717894/overviewRole: Role: Role:
                URI : http://loop.frontiersin.org/people/497913/overviewRole: Role: Role: Role: Role:
                URI : http://loop.frontiersin.org/people/1643277/overviewRole: Role: Role: Role:
                URI : http://loop.frontiersin.org/people/2233464/overviewRole: Role: Role: Role: Role: Role:
                URI : http://loop.frontiersin.org/people/1659898/overviewRole: Role: Role: Role:
                URI : http://loop.frontiersin.org/people/2684650/overviewRole: Role: Role: Role:
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                06 September 2024
                2024
                : 18
                : 1391465
                Affiliations
                [1] 1Department of Computer Application, Faculty of Engineering & IT, Integral University , Lucknow, India
                [2] 2Department of Computer Science, Faculty of Science, Aligarh Muslim University , Aligarh, India
                [3] 3Department of Computer Science, College of Engineering and Computer Science, Jazan University , Jazan, Saudi Arabia
                Author notes

                Edited by: Peter Kokol, University of Maribor, Slovenia

                Reviewed by: Miren Altuna, Fundacion CITA Alzheimer, Spain

                Bojan Žlahtič, University of Maribor, Slovenia

                *Correspondence: Abdullah Sheneamer asheneamer@ 123456jazanu.edu.sa
                Article
                10.3389/fnins.2024.1391465
                11412962
                39308946
                1d45f827-60de-4990-af8a-fb79b013cdcb
                Copyright © 2024 Khan, Zubair, Shuaib, Sheneamer, Alam and Assiri.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 February 2024
                : 12 August 2024
                Page count
                Figures: 6, Tables: 18, Equations: 0, References: 89, Pages: 33, Words: 19954
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia through Project number GSSRD-24.
                Categories
                Neuroscience
                Original Research
                Custom metadata
                Translational Neuroscience

                Neurosciences
                alzheimer's disease,biomarker,early diagnosis,drug,hybrid clinical model,machine learning,multinomial classification,protein

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