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Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia

T. R. Sivapriya, A. R. Nadira Banu Kamal, P. Ranjit Jeba Thangaiah
2015 Computational and Mathematical Methods in Medicine  
A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques.  ...  Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5.  ...  building the classifier.  ... 
doi:10.1155/2015/676129 pmid:26576199 pmcid:PMC4632180 fatcat:gggs4ygdxfgstdc7j3knpdxri4

Machine Learning for the Preliminary Diagnosis of Dementia

Fubao Zhu, Xiaonan Li, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chiu, Weihua Zhou
2020 Scientific Programming  
We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based  ...  The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.  ...  Discussion e purpose of this study was to provide a new clinical tool based on machine learning for the early diagnosis of dementia.  ... 
doi:10.1155/2020/5629090 fatcat:cus4bwk4ezax3ikncb5r4fn6he

Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer's Disease Based on Deep Learning and Hybrid Methods

Badiea Abdulkarem Mohammed, Ebrahim Mohammed Senan, Taha H. Rassem, Nasrin M. Makbol, Adwan Alownie Alanazi, Zeyad Ghaleb Al-Mekhlafi, Tariq S. Almurayziq, Fuad A. Ghaleb
2021 Electronics  
Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer's disease.  ...  All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia.  ...  Acknowledgments: We would like to acknowledge the Scientific Research Deanship at the University of Ha'il, Saudi Arabia, for funding this research.  ... 
doi:10.3390/electronics10222860 fatcat:2vgkbev6ifdp7adweaoesanr3a

A Comprehensive Performance Analysis of Neurodegenerative diseases Incidence based on Epidemiological Study in the Female subjects over varied data

Afreen Khan, Swaleha Zubair, Samreen Khan
2021 Advances in Distributed Computing and Artificial Intelligence Journal  
The application of machine learning has the potential to enormously augment the reach of neurodegenerative care thus building it more proficient.  ...  Harnessing this data using machine learning tools and techniques, can put scientists and physicians in the lead research position in this area.  ...  The class of subjects is segmented amongst 4 classes, from no dementia to very mild, mild and moderate dementia.  ... 
doi:10.14201/adcaij2021102183196 fatcat:yclwlyatwra25b7qqf3vd4fjna

Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review

Ana Luiza Dallora, Shahryar Eivazzadeh, Emilia Mendes, Johan Berglund, Peter Anderberg
2016 Procedia Computer Science  
CONCLUSIONS: The systematic literature review showed clear trends in prognosis of dementia research in what concerns machine learning techniques and microsimulation.  ...  , for studies that used machine learning, and cost estimation for the microsimulation ones.  ...  • RQ 3: What are the goals of the studies that employ machine learning or microsimulation techniques for prognosis of dementia and comorbidities?  ... 
doi:10.1016/j.procs.2016.09.185 fatcat:fykyl7czynantkx5debhld7gka

The Growing Role of Complex Sensor Systems and Algorithmic Pattern Recognition for Vascular Dementia Onset

Janna Madden, Arshia Khan
2019 International Journal of Advanced Computer Science and Applications  
This compilation of works presents current a framework for investigating the various behavioral and physiological metrics as well as potential avenues for further investigating of sensor system and algorithmic  ...  Many computational systems have been proposed for the evaluation these early signs of Vascular Dementia.  ...  Determine time of Dementia Onset As demonstrated by the Grahams, Emery and Hodges study [28] , these assessments can be used a source for data for building statistical or machine learning models that  ... 
doi:10.14569/ijacsa.2019.0100227 fatcat:mw22fdx2rfcitcvq5adpzisddy

Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks

Jyoti Islam, Yanqing Zhang
2018 Brain Informatics  
Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis.  ...  He has served as a reviewer for over 70 international journals and as a program committee member for over 150 international conferences and workshops.  ...  Fig. 1 1 Example of different brain MRI images presenting different Alzheimer's disease stages. a Non-demented; b Very mild dementia; c Mild dementia; d Moderate dementia Fig. 2 2 Common building block  ... 
doi:10.1186/s40708-018-0080-3 pmid:29881892 fatcat:zybwvmkiwfh5bedzaisbooxbry

Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool to Stage Dementia Severity (Preprint)

Aaqib Shehzad, Kenneth Rockwood, Justin Stanley, Taylor Dunn, Susan E Howlett
2020 Journal of Medical Internet Research  
A model trained with a support vector machine learning algorithm using a one-versus-rest approach showed the best performance.  ...  The best performing algorithm was used to train a model optimized for balanced accuracy.  ...  represented stage (mild dementia) was undersampled in the machine learning pipeline.  ... 
doi:10.2196/20840 pmid:33174853 fatcat:4pdutdovi5hj3otyz5ozkheely

Alzheimer's Disease Classification Using Deep CNN

Shikha Agrawal, Neha Sunil Pandharkar, Pooja Arvind Khandelwal, Pratiksha Ashok Pandhare, Janhavi Sanjay Deoghare
2021 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
Especially in the world, the deep learning algorithm has become a technique of choice for analyzing medical images rapidly.  ...  A better diagnostic needs to be developed, which is addressed in this paper. We concentrate on Alzheimer's disease in this article and discuss different methods are available to detect Alzheimer's.  ...  Support Vector Machine (SVM) SVM plays an important role in the development of machine learning model. It is a controlled model of learning machines and their related study algorithms of learning.  ... 
doi:10.32628/cseit217371 fatcat:6tgqbmj2ordkrmytqgetycyp4e

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol

Hao Luo, Kui Kai Lau, Gloria H Y Wong, Wai-Chi Chan, Henry K F Mak, Qingpeng Zhang, Martin Knapp, Ian C K Wong
2020 BMJ Open  
Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models.  ...  Dementia is a group of disabling disorders that can be devastating for persons living with it and for their families.  ...  Machine learning is a very broadly defined method that automates analytical model building.  ... 
doi:10.1136/bmjopen-2020-043487 pmid:33444218 pmcid:PMC7678375 fatcat:tpbxyirp2fagzgs7xfrl6iy5ku

NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia

Pai-Yi Chiu, Haipeng Tang, Cheng-Yu Wei, Chaoyang Zhang, Guang-Uei Hung, Weihua Zhou, Simone Reppermund
2019 PLoS ONE  
Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia.  ...  Conclusions NMD-12 derived from machine learning is a simple and effective screening tool for discriminating NC, MCI, and dementia.  ...  in discriminating NC, MCI, very mild dementia (VMD) and dementia.  ... 
doi:10.1371/journal.pone.0213430 pmid:30849106 pmcid:PMC6407752 fatcat:jvqsqgwbkrdvljpght6m7x5mpi

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease

Chun-Hung Chang, Chieh-Hsin Lin, Hsien-Yuan Lane
2021 International Journal of Molecular Sciences  
Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy  ...  Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD.  ...  The sponsors were not involved in the design of the study; the collection, analysis, and interpretation of the data; the writing of the report; and the decision to submit the article for publication.  ... 
doi:10.3390/ijms22052761 pmid:33803217 fatcat:33ixois7qjgjtmdwik3vla5xke


Sandhya Joshi, Vibhudendra Simha GG, Deepa Shenoy P, Venugopal KR, Patnaik LM
2010 International Journal of Bioinformatics Research  
This research work presents different models for the classification of different stages of Alzheimer's disease using various machine learning methods such as Neural Networks, Multilayer Perceptron, Bagging  ...  Based on the outcome of classification accuracies, various management and treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild, moderate and severe AD were  ...  Hodes Director of National Institute on Aging (NIA), the component of the National Institutes of Health, USA for providing useful information.  ... 
doi:10.9735/0975-3087.2.1.44-52 fatcat:47oehbocnnggvhxyqawjo6oytu

Alzheimer Disease diagnosis using Machine Learning Strategies

G. Stalin, Srujana Kurasakutla, Jagadeesh Palli, Maddi Gnana
2020 International Journal of Computer Applications  
However, clinicians and researchers will need to use machine learning techniques that can accurately predict a patient's progress from mild cognitive impairment to dementia in order to reach that stage  ...  The outcome of this paper will help us to detect the disease in earlier stages by finding the accuracy of machine learning algorithms and determining the attribute that helped us in giving a maximum accuracy  ...  However, clinicians and researchers will need to use machine learning techniques that can accurately predict a patient's progress from mild cognitive impairment to dementia in order to reach that stage  ... 
doi:10.5120/ijca2020920042 fatcat:j64awsk7wrgprirj7jkizzcgxe

Deep Learning techniques for effective diagnosis of Alzheimer's disease using MRI images

Prajakta Tambe, Rutuja Saigaonkar, Nidhi Devadiga, Pallavi H. Chitte, M.D. Patil, V.A. Vyawahare
2021 ITM Web of Conferences  
But very little has been done in the use of deep learning strategies to turn up and differentiate Alzheimer's disease.  ...  Recent advances in deep learning from a computer perspective have advanced in that research.  ...  No dementia 2. Very mild AD. 3. Mild AD. 4.  ... 
doi:10.1051/itmconf/20214003021 fatcat:gcl6wik5njecbip63v2vzinzw4
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