{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:52:35Z","timestamp":1777697555744,"version":"3.51.4"},"reference-count":57,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2023,5,15]]},"abstract":"<jats:p>Dementia is a brain condition that impairs the cognitive abilities of an individual. Mild cognitive impairment is a mediator phase of healthy and dementia controls. The motivation of this study is to predict dementia using magnetic resonance imaging data, which is significant for the diagnosis of normal control and dementia patients. The proposed model leverages effective methods like Discrete Wavelet Transform, Bag of Features, and Support Vector Machine. The four wavelets haar, Daubechies, symlets, and coiflets are used for image compression. The results of the proposed data intelligence model are promising in terms of accuracy which is 92.32% which is better than the recently proposed models. Also, the proposed data intelligence model is compared with the models which may use curvelet transform, and shearlet transform and with the methods which have gone without using DWT transforms. The comparisons have also been made with the models that have used other prevalent techniques like Principal Component Analysis, Fisher Discriminant Ratio, and Gray Level Co-occurrence Matrix. The outcomes support the usage of each technique in the suggested data intelligence paradigm.<\/jats:p>","DOI":"10.3233\/idt-220256","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T11:35:15Z","timestamp":1675769715000},"page":"543-555","source":"Crossref","is-referenced-by-count":2,"title":["BoF-SVM-based data intelligence model for detecting dementia"],"prefix":"10.1177","volume":"17","author":[{"given":"Deepika","family":"Bansal","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The NorthCap University, Gurugram, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kavita","family":"Khanna","sequence":"additional","affiliation":[{"name":"Delhi Skill and Entrepreneurship University, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rita","family":"Chhikara","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The NorthCap University, Gurugram, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rakesh Kumar","family":"Dua","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Fortis Hospital, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajeev","family":"Malhotra","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Max Super Speciality Hospital, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/IDT-220256_ref1","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack","year":"2008","journal-title":"Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine"},{"key":"10.3233\/IDT-220256_ref2","unstructured":"ADNI. Usc.edu. Available from: https:\/\/adni.loni.usc.edu\/data-samples\/access-data\/."},{"key":"10.3233\/IDT-220256_ref3","unstructured":"OASIS brains\u00a0\u2013 Open Access Series of Imaging Studies. Oasis-brains.org. Available from: https:\/\/www.oasis-brains.org\/."},{"issue":"12","key":"10.3233\/IDT-220256_ref4","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","article-title":"Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults","volume":"22","author":"Marcus","year":"2010","journal-title":"Journal of cognitive neuroscience"},{"issue":"9","key":"10.3233\/IDT-220256_ref5","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults","volume":"19","author":"Marcus","year":"2007","journal-title":"Journal of cognitive neuroscience"},{"key":"10.3233\/IDT-220256_ref6","first-page":"583","article-title":"Deep learning-based feature representation for AD\/MCI classification","author":"Suk","year":"2013","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"10.3233\/IDT-220256_ref7","first-page":"143","article-title":"Deep-learning-based classification of FDG-PET data for Alzheimer\u2019s disease categories","volume":"10572","author":"Singh","year":"2017","journal-title":"13th International Conference on Medical Information Processing and Analysis"},{"key":"10.3233\/IDT-220256_ref8","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1201\/9780429444272-66","article-title":"A study on dementia using machine learning techniques","author":"Bansal","year":"2019","journal-title":"Communication and Computing Systems"},{"key":"10.3233\/IDT-220256_ref9","first-page":"61","article-title":"A systematic literature review of deep learning for detecting dementia","author":"Bansal","year":"2021","journal-title":"Proceedings of the Second International Conference on Information Management and Machine Intelligence"},{"key":"10.3233\/IDT-220256_ref10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.2528\/PIER13121310","article-title":"Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree","volume":"144","author":"Zhang","year":"2014","journal-title":"Progress In Electromagnetics Research"},{"issue":"2","key":"10.3233\/IDT-220256_ref11","first-page":"339","article-title":"Compression of medical images using wavelet transforms","volume":"2","author":"Ruchika","year":"2012","journal-title":"International Journal of Soft Computing and Engineering"},{"issue":"5800","key":"10.3233\/IDT-220256_ref12","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1126\/science.1132813","article-title":"100 years and counting: prospects for defeating Alzheimer\u2019s disease","volume":"314","author":"Roberson","year":"2006","journal-title":"Science"},{"issue":"2","key":"10.3233\/IDT-220256_ref13","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/83.136597","article-title":"Image coding using wavelet transform","volume":"1","author":"Antonini","year":"1992","journal-title":"IEEE Transactions on image processing"},{"issue":"1","key":"10.3233\/IDT-220256_ref14","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/83.481666","article-title":"Image compression using wavelet transform and multiresolution decomposition","volume":"5","author":"Averbuch","year":"1996","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.3233\/IDT-220256_ref15","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TENCON.2004.1414384","article-title":"Wavelet filters comparison for ultrasonic image compression","author":"Udomhunsakul","year":"2004","journal-title":"2004 IEEE Region 10 Conference TENCON 2004"},{"key":"10.3233\/IDT-220256_ref16","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.procs.2018.05.102","article-title":"Comparative analysis of various machine learning algorithms for detecting dementia","volume":"132","author":"Bansal","year":"2018","journal-title":"Procedia computer science"},{"key":"10.3233\/IDT-220256_ref17","doi-asserted-by":"crossref","unstructured":"Bansal D, Khanna K, Chhikara R, Dua RK, Malhotra R. Analysis of classification & feature selection techniques for detecting dementia. In: Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM). Amity University Rajasthan, Jaipur-India; 2019 Feb 26.","DOI":"10.2139\/ssrn.3356886"},{"issue":"4","key":"10.3233\/IDT-220256_ref18","first-page":"611","article-title":"Analysis of Univariate and Multivariate Filters Towards the Early Detection of Dementia","volume":"15","author":"Bansal","year":"2022","journal-title":"Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science)"},{"issue":"6","key":"10.3233\/IDT-220256_ref19","first-page":"1015","article-title":"U-Net-ASPP: U-Net based on atrous spatial pyramid pooling model for medical image segmentation in COVID-19","volume":"25","author":"Qiu","year":"2022","journal-title":"Journal of Applied Science and Engineering"},{"issue":"4","key":"10.3233\/IDT-220256_ref20","first-page":"465","article-title":"Web Scraping Tool For Newspapers And Images Data Using Jsonify","volume":"26","author":"Niu","year":"2022","journal-title":"Journal of Applied Science and Engineering"},{"issue":"4","key":"10.3233\/IDT-220256_ref21","first-page":"733","article-title":"Research on fault identification method based on multi-resolution permutation entropy and ABC-SVM","volume":"25","author":"Yang","year":"2021","journal-title":"Journal of Applied Science and Engineering"},{"issue":"1","key":"10.3233\/IDT-220256_ref22","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.bspc.2006.05.002","article-title":"Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network","volume":"1","author":"Chaplot","year":"2006","journal-title":"Biomedical signal processing and control"},{"issue":"2","key":"10.3233\/IDT-220256_ref23","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.dsp.2009.07.002","article-title":"Hybrid intelligent techniques for MRI brain images classification","volume":"20","author":"El-Dahshan","year":"2010","journal-title":"Digital signal processing"},{"key":"10.3233\/IDT-220256_ref24","doi-asserted-by":"crossref","first-page":"369","DOI":"10.2528\/PIER12061410","article-title":"An MR brain images classifier via principal component analysis and kernel support vector machine","volume":"130","author":"Zhang","year":"2012","journal-title":"Progress In Electromagnetics Research"},{"issue":"2","key":"10.3233\/IDT-220256_ref25","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1002\/ima.22135","article-title":"3d discrete wavelet transform for computer aided diagnosis of A lzheimer\u2019s disease using t1-weighted brain MRI","volume":"25","author":"Aggarwal","year":"2015","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"10.3233\/IDT-220256_ref26","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.bspc.2015.05.014","article-title":"Detection of Alzheimer\u2019s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC","volume":"21","author":"Zhang","year":"2015","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.3233\/IDT-220256_ref27","doi-asserted-by":"crossref","unstructured":"Jha D, Kim JI, Kwon GR. Diagnosis of Alzheimer\u2019s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network. Journal of Healthcare Engineering. 2017 Jun 21; 2017.","DOI":"10.1155\/2017\/9060124"},{"issue":"1","key":"10.3233\/IDT-220256_ref28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-020-1055-x","article-title":"A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment","volume":"20","author":"Bhasin","year":"2020","journal-title":"BMC Medical Informatics and Decision Making"},{"key":"10.3233\/IDT-220256_ref29","first-page":"79","article-title":"Discrimination between alzheimer\u2019s disease and control group in MR-images based on texture analysis using artificial neural network","author":"Torabi","year":"2006","journal-title":"2006 International Conference on Biomedical and Pharmaceutical Engineering"},{"key":"10.3233\/IDT-220256_ref30","unstructured":"Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation, Fundamental Papers in Wavelet Theory."},{"issue":"1","key":"10.3233\/IDT-220256_ref31","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern recognition"},{"issue":"9","key":"10.3233\/IDT-220256_ref32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1428-9","article-title":"Automated detection of Alzheimer\u2019s disease using brain MRI images\u00a0\u2013 a study with various feature extraction techniques","volume":"43","author":"Acharya","year":"2019","journal-title":"Journal of Medical Systems"},{"key":"10.3233\/IDT-220256_ref33","first-page":"1","article-title":"Local discriminative characterization of MRI for Alzheimer\u2019s disease","author":"Chaddad","year":"2016","journal-title":"2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)"},{"key":"10.3233\/IDT-220256_ref34","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/IST.2009.5071627","article-title":"Compression of MRI images using the discrete wavelet transform and improved parameter free Bayesian restoration techniques","author":"Karras","year":"2009","journal-title":"2009 IEEE International Workshop on Imaging Systems and Techniques"},{"key":"10.3233\/IDT-220256_ref35","doi-asserted-by":"crossref","first-page":"134971","DOI":"10.1016\/j.neulet.2020.134971","article-title":"Deep learning based mild cognitive impairment diagnosis using structure MR images","volume":"730","author":"Jiang","year":"2020","journal-title":"Neuroscience Letters"},{"key":"10.3233\/IDT-220256_ref36","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.procs.2020.03.190","article-title":"Classification of magnetic resonance images using bag of features for detecting dementia","volume":"167","author":"Bansal","year":"2020","journal-title":"Procedia Computer Science"},{"key":"10.3233\/IDT-220256_ref37","doi-asserted-by":"crossref","unstructured":"Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993 Nov.","DOI":"10.1212\/WNL.43.11.2412-a"},{"key":"10.3233\/IDT-220256_ref38","unstructured":"NITRC: The fMRI data center: Tool\/resource info. Nitrc.org. Available from: https:\/\/www.nitrc.org\/projects\/fmridatacenter\/."},{"key":"10.3233\/IDT-220256_ref39","unstructured":"MRIcro software guide. Cas.sc.edu. Available from: https:\/\/people.cas.sc.edu\/rorden\/mricro\/mricro.html."},{"key":"10.3233\/IDT-220256_ref40","unstructured":"Frazier MW. An introduction to wavelets through linear algebra. Springer Science & Business Media; 2006 Apr 6."},{"key":"10.3233\/IDT-220256_ref41","doi-asserted-by":"crossref","unstructured":"Lahmiri S, Boukadoum M. Hybrid discrete wavelet transform and gabor filter banks processing for features extraction from biomedical images. Journal of Medical Engineering. 2013; 2013.","DOI":"10.1155\/2013\/104684"},{"key":"10.3233\/IDT-220256_ref42","unstructured":"O\u2019Hara S, Draper BA. Introduction to the bag of features paradigm for image classification and retrieval. arXiv preprint arXiv:1101.3354. 2011 Jan 17."},{"issue":"3","key":"10.3233\/IDT-220256_ref43","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Machine Learning"},{"issue":"2","key":"10.3233\/IDT-220256_ref44","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00234-008-0463-x","article-title":"Support vector machine-based classification of Alzheimer\u2019s disease from whole-brain anatomical MRI","volume":"51","author":"Magnin","year":"2009","journal-title":"Neuroradiology"},{"key":"10.3233\/IDT-220256_ref45","first-page":"60","article-title":"3D-DWT improves prediction of AD and MCI","author":"Wang","year":"2015","journal-title":"First International Conference on Information Science and Electronic Technology (ISET 2015)"},{"key":"10.3233\/IDT-220256_ref46","first-page":"1","article-title":"Robust algorithm for early detection of Alzheimer\u2019s disease using multiple feature extractions","author":"Mathew","year":"2016","journal-title":"2016 IEEE Annual India Conference (INDICON)"},{"issue":"2","key":"10.3233\/IDT-220256_ref47","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/TCBB.2016.2635144","article-title":"Classification of Alzheimer\u2019s disease using whole brain hierarchical network","volume":"15","author":"Liu","year":"2016","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"10.3233\/IDT-220256_ref48","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.bspc.2018.10.010","article-title":"A hybrid feature extraction approach for brain MRI classification based on Bag-of-words","volume":"48","author":"Ayadi","year":"2019","journal-title":"Biomedical Signal Processing and Control"},{"issue":"1","key":"10.3233\/IDT-220256_ref49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: a survey","volume":"15","author":"Sarvamangala","year":"2022","journal-title":"Evolutionary Intelligence"},{"key":"10.3233\/IDT-220256_ref50","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.future.2018.03.023","article-title":"Automated system for the detection of thoracolumbar fractures using a CNN architecture","volume":"85","author":"Raghavendra","year":"2018","journal-title":"Future Generation Computer Systems"},{"key":"10.3233\/IDT-220256_ref51","first-page":"349","article-title":"Image pre-processing and feature extraction techniques for magnetic resonance brain image analysis","author":"Jude Hemanth","year":"2012","journal-title":"International Conference on Future Generation Communication and Networking"},{"key":"10.3233\/IDT-220256_ref52","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.1109\/ACCESS.2018.2885639","article-title":"A modified deep convolutional neural network for abnormal brain image classification","volume":"7","author":"Hemanth","year":"2018","journal-title":"IEEE Access"},{"key":"10.3233\/IDT-220256_ref53","doi-asserted-by":"crossref","first-page":"108046","DOI":"10.1016\/j.measurement.2020.108046","article-title":"Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning","volume":"165","author":"Jain","year":"2020","journal-title":"Measurement"},{"issue":"3","key":"10.3233\/IDT-220256_ref54","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/s00521-018-03974-0","article-title":"An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network","volume":"32","author":"Hemanth","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/IDT-220256_ref55","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.asoc.2019.02.036","article-title":"Deep learning based enhanced tumor segmentation approach for MR brain images","volume":"78","author":"Mittal","year":"2019","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDT-220256_ref56","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.cogsys.2018.12.015","article-title":"Convolutional neural network based Alzheimer\u2019s disease classification from magnetic resonance brain images","volume":"57","author":"Jain","year":"2019","journal-title":"Cognitive Systems Research"},{"key":"10.3233\/IDT-220256_ref57","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jocs.2017.02.006","article-title":"Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network","volume":"20","author":"Tan","year":"2017","journal-title":"Journal of Computational Science"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-220256","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:23:33Z","timestamp":1777454613000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-220256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":57,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/idt-220256","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,15]]}}}