{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:58:55Z","timestamp":1743094735731,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811994821"},{"type":"electronic","value":"9789811994838"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-19-9483-8_1","type":"book-chapter","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T11:02:15Z","timestamp":1685185335000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Early Prediction and Analysis of DTI and MRI-Based Alzheimer\u2019s Disease Through Machine Learning Techniques"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8512-5747","authenticated-orcid":false,"given":"Amira","family":"Mahjabeen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4692-7517","authenticated-orcid":false,"given":"Md Rajib","family":"Mia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9561-3613","authenticated-orcid":false,"given":"F. N. U.","family":"Shariful","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9306-9637","authenticated-orcid":false,"given":"Nuruzzaman","family":"Faruqui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2962-8515","authenticated-orcid":false,"given":"Imran","family":"Mahmud","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"1_CR1","unstructured":"ADNI. Alzheimer\u2019s disease neuroimaging initiative. http:\/\/adni.loni.usc.edu\/. Accessed 12 Jan 2022"},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"Almubark I, Chang L, Nguyen T, Turner RS, Jiang X (2019) Early detection of Alzheimer\u2019s disease using patient neuropsychological and cognitive data and machine learning techniques. In: 2019 IEEE international conference on big data (Big Data), pp 5971\u20135973. https:\/\/doi.org\/10.1109\/BigData47090.2019.9006583","DOI":"10.1109\/BigData47090.2019.9006583"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"Almubark I, Alsegehy S, Jiang X, Chang L-C (2020) Early detection of mild cognitive impairment using neuropsychological data and machine learning techniques. In: 2020 IEEE conference on big data and analytics (ICBDA). https:\/\/doi.org\/10.1109\/icbda50157.2020.92897","DOI":"10.1109\/icbda50157.2020.92897"},{"key":"1_CR4","doi-asserted-by":"publisher","unstructured":"2019 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement 15(3):321\u2013387. https:\/\/doi.org\/10.1016\/j.jalz.2019.01.010","DOI":"10.1016\/j.jalz.2019.01.010"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Badnjevic A, \u0160krbi\u0107 R, Gurbeta Pokvi\u0107 L (2020) [IFMBE Proceedings] CMBEBIH 2019, vol 73 (Proceedings of the international conference on medical and biological engineering, 16\u201318 May 2019, Banja Luka, Bosnia and Herzegovina). Automatic detection of Alzheimer disease based on histogram and random forest, pp 91\u201396. https:\/\/doi.org\/10.1007\/978-3-030-17971-7_14","DOI":"10.1007\/978-3-030-17971-7_14"},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Battineni G, Chintalapudi N, Amenta F (2019) Machine learning in medicine: performance calculation of dementia prediction by support vector machines (SVM). Inform Med Unlocked 100200. https:\/\/doi.org\/10.1016\/j.imu.2019.100200","DOI":"10.1016\/j.imu.2019.100200"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Benyoussef EM, Elbyed A, El Hadiri H (2017) Data mining approaches for Alzheimer\u2019s disease diagnosis. Lecture notes in computer science, pp 619\u2013631. https:\/\/doi.org\/10.1007\/978-3-319-68179-5_54","DOI":"10.1007\/978-3-319-68179-5_54"},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Bucholc M, Ding X, Wang H, Glass DH, Wang H, Prasad G, Wong-Lin K et al (2019) A practical computerized decision support system for predicting the severity of Alzheimer\u2019s disease of an individual. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2019.04.022","DOI":"10.1016\/j.eswa.2019.04.022"},{"key":"1_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107944","volume":"116","author":"Y Chen","year":"2021","unstructured":"Chen Y, Xia Y (2021) Iterative sparse and deep learning for accurate diagnosis of Alzheimer\u2019s disease. Pattern Recogn 116:107944. https:\/\/doi.org\/10.1016\/j.patcog.2021.107944","journal-title":"Pattern Recogn"},{"key":"1_CR10","doi-asserted-by":"publisher","unstructured":"Dahiwade D, Patle G, Meshram E (2019) Designing disease prediction model using machine learning approach. In: 2019 3rd international conference on computing methodologies and communication (ICCMC). https:\/\/doi.org\/10.1109\/iccmc.2019.8819782","DOI":"10.1109\/iccmc.2019.8819782"},{"key":"1_CR11","doi-asserted-by":"publisher","unstructured":"De A, Chowdhury AS (2020) DTI based Alzheimer disease classification with rank modulated fusion of CNNs and random forest. Expert Syst Appl 114338. https:\/\/doi.org\/10.1016\/j.eswa.2020.114338","DOI":"10.1016\/j.eswa.2020.114338"},{"issue":"1","key":"1_CR12","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1109\/JBHI.2020.2984355","volume":"25","author":"CS Eke","year":"2021","unstructured":"Eke CS, Jammeh E, Li X, Carroll C, Pearson S, Ifeachor E (2021) Early detection of Alzheimer\u2019s disease with blood plasma proteins using support vector machines. IEEE J Biomed Health Inform 25(1):218\u2013226. https:\/\/doi.org\/10.1109\/JBHI.2020.2984355","journal-title":"IEEE J Biomed Health Inform"},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1007\/s00521-019-04495-0","volume":"32","author":"Z Fan","year":"2020","unstructured":"Fan Z, Xu F, Qi X et al (2020) Classification of Alzheimer\u2019s disease based on brain MRI and machine learning. Neural Comput Appl 32:1927\u20131936. https:\/\/doi.org\/10.1007\/s00521-019-04495-0","journal-title":"Neural Comput Appl"},{"key":"1_CR14","doi-asserted-by":"publisher","unstructured":"Ghoraani B, Boettcher LN, Hssayeni MD, Rosenfeld A, Tolea MI, Galvin JE (2021) Detection of mild cognitive impairment and Alzheimer\u2019s disease using dual-task gait assessments and machine learning. Biomed Signal Process Control 64:102249. https:\/\/doi.org\/10.1016\/j.bspc.2020.102249","DOI":"10.1016\/j.bspc.2020.102249"},{"key":"1_CR15","doi-asserted-by":"publisher","unstructured":"Johnson KA, Fox NC, Sperling RA, Klunk WE (2012) Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med 2(4):a006213. https:\/\/doi.org\/10.1101\/cshperspect.a006213. PMID: 22474610. PMCID: PMC3312396","DOI":"10.1101\/cshperspect.a006213"},{"key":"1_CR16","unstructured":"Karatekin \u00c7 (2021) Early detection of Alzheimer\u2019s disease using data mining: comparison of ensemble feature selection approaches"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Kruthika KR, Rajeswari, Maheshappa HD (2019) Multistage classifier-based approach for Alzheimer\u2019s disease prediction and retrieval. Inform Med Unlocked 14:34\u201342. https:\/\/doi.org\/10.1016\/j.imu.2018.12.003","DOI":"10.1016\/j.imu.2018.12.003"},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s41870-017-0057-0","volume":"10","author":"N Kulkarni","year":"2018","unstructured":"Kulkarni N (2018) Use of complexity based features in diagnosis of mild Alzheimer disease using EEG signals. Int J Inf Tecnol 10:59\u201364. https:\/\/doi.org\/10.1007\/s41870-017-0057-0","journal-title":"Int J Inf Tecnol"},{"key":"1_CR19","doi-asserted-by":"publisher","unstructured":"Liu L, Zhao S, Chen H, Wang A (2019) A new machine learning method for identifying Alzheimer\u2019s disease. Simul Model Pract Theory 102023. https:\/\/doi.org\/10.1016\/j.simpat.2019.102023","DOI":"10.1016\/j.simpat.2019.102023"},{"key":"1_CR20","doi-asserted-by":"publisher","unstructured":"Lodha P, Talele A, Degaonkar K (2018) Diagnosis of Alzheimer\u2019s disease using machine learning. In: 2018 fourth international conference on computing communication control and automation (ICCUBEA). https:\/\/doi.org\/10.1109\/iccubea.2018.8697386","DOI":"10.1109\/iccubea.2018.8697386"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Madiwalar S (2020) Classification and investigation of Alzheimer disease using machine learning algorithms. Biochem Biophys Res Commun","DOI":"10.21786\/bbrc\/13.13\/3"},{"issue":"1","key":"1_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-020-09773-x","volume":"13","author":"M Mahmud","year":"2021","unstructured":"Mahmud M, Kaiser MS, McGinnity TM, Hussain A (2021) Deep learning in mining biological data. Cogn Comput 13(1):1\u201333","journal-title":"Cogn Comput"},{"key":"1_CR23","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s00530-021-00797-3","volume":"28","author":"S Naz","year":"2022","unstructured":"Naz S, Ashraf A, Zaib A (2022) Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimedia Syst 28:85\u201394. https:\/\/doi.org\/10.1007\/s00530-021-00797-3","journal-title":"Multimedia Syst"},{"key":"1_CR24","doi-asserted-by":"publisher","unstructured":"Neelaveni J, Devasana MSG (2020) Alzheimer disease prediction using machine learning algorithms. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), pp 101\u2013104. https:\/\/doi.org\/10.1109\/ICACCS48705.2020.9074248","DOI":"10.1109\/ICACCS48705.2020.9074248"},{"issue":"1","key":"1_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-020-00112-2","volume":"7","author":"MBT Noor","year":"2020","unstructured":"Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer\u2019s disease, Parkinson\u2019s disease and schizophrenia. Brain Inform 7(1):1\u201321","journal-title":"Brain Inform"},{"key":"1_CR26","doi-asserted-by":"publisher","unstructured":"Oishi K, Mielke MM, Albert M, Lyketsos CG, Mori S (2011) DTI analyses and clinical applications in Alzheimer\u2019s disease. J Alzheimers Dis 26(Suppl 3):287\u2013296. https:\/\/doi.org\/10.3233\/JAD-2011-0007. PMID: 21971468. PMCID: PMC3294372","DOI":"10.3233\/JAD-2011-0007"},{"key":"1_CR27","doi-asserted-by":"publisher","unstructured":"Perera S, Hewage K, Gunarathne C, Navarathna R, Herath D, Ragel RG (2020) Detection of novel biomarker genes of Alzheimer\u2019s disease using gene expression data. In: 2020 Moratuwa engineering research conference (MERCon), pp 1\u20136. https:\/\/doi.org\/10.1109\/MERCon50084.2020.9185336","DOI":"10.1109\/MERCon50084.2020.9185336"},{"key":"1_CR28","doi-asserted-by":"publisher","unstructured":"Rallabandi VPS, Tulpule K, Gattu M (2020) Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer\u2019s disease using structural MRI analysis. Inform Med Unlocked 100305. https:\/\/doi.org\/10.1016\/j.imu.2020.100305","DOI":"10.1016\/j.imu.2020.100305"},{"key":"1_CR29","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.procs.2020.01.071","volume":"165","author":"M Rohini","year":"2019","unstructured":"Rohini M, Surendran D (2019) Classification of neurodegenerative disease stages using ensemble machine learning classifiers. Procedia Comput Sci 165:66\u201373. https:\/\/doi.org\/10.1016\/j.procs.2020.01.071","journal-title":"Procedia Comput Sci"},{"key":"1_CR30","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1007\/s00500-020-05292-x","volume":"25","author":"M Rohini","year":"2021","unstructured":"Rohini M, Surendran D (2021) Toward Alzheimer\u2019s disease classification through machine learning. Soft Comput 25:2589\u20132597. https:\/\/doi.org\/10.1007\/s00500-020-05292-x","journal-title":"Soft Comput"},{"key":"1_CR31","doi-asserted-by":"publisher","unstructured":"Shah A, Lalakiya D, Desai S, Shreya, Patel V (2020) Early detection of Alzheimer\u2019s disease using various machine learning techniques: a comparative study. In: 2020 4th international conference on trends in electronics and informatics (ICOEI) (48184). https:\/\/doi.org\/10.1109\/icoei48184.2020.9142975","DOI":"10.1109\/icoei48184.2020.9142975"},{"key":"1_CR32","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1007\/s00062-021-01057-7","volume":"31","author":"P Talwar","year":"2021","unstructured":"Talwar P, Kushwaha S, Chaturvedi M et al (2021) Systematic review of different neuroimaging correlates in mild cognitive impairment and Alzheimer\u2019s disease. Clin Neuroradiol 31:953\u2013967. https:\/\/doi.org\/10.1007\/s00062-021-01057-7","journal-title":"Clin Neuroradiol"},{"key":"1_CR33","doi-asserted-by":"publisher","unstructured":"Thapa S, Singh P, Jain DK, Bharill N, Gupta A, Prasad M (2020) Data-driven approach based on feature selection technique for early diagnosis of Alzheimer\u2019s disease. In: 2020 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207359","DOI":"10.1109\/IJCNN48605.2020.9207359"},{"key":"1_CR34","doi-asserted-by":"publisher","unstructured":"Thushara A, UshaDevi Amma C, John A, Saju R (2020) Multimodal MRI based classification and prediction of Alzheimer\u2019s disease using random forest ensemble. In: 2020 advanced computing and communication technologies for high performance applications (ACCTHPA), pp 249\u2013256. https:\/\/doi.org\/10.1109\/ACCTHPA49271.2020.9213211","DOI":"10.1109\/ACCTHPA49271.2020.9213211"},{"key":"1_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.crbeha.2021.100044","volume":"2","author":"MS Zulfiker","year":"2021","unstructured":"Zulfiker MS, Kabir N, Biswas AA, Nazneen T, Uddin MS (2021) An in-depth analysis of machine learning approaches to predict depression. Curr Res Behav Sci 2:100044. https:\/\/doi.org\/10.1016\/j.crbeha.2021.100044","journal-title":"Curr Res Behav Sci"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-9483-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T11:09:08Z","timestamp":1685185748000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-9483-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789811994821","9789811994838"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-9483-8_1","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"28 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}