{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:27:18Z","timestamp":1788780438069,"version":"build-2803163510"},"reference-count":26,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Predicting the\n                      \n                      \n                      course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study presents a novel method for Parkinson's disease prediction using freely accessible resources. The suggested approach starts with band-pass filter data preprocessing and uses Empirical Mode Decomposition (EMD) for feature extraction. Then, for classification, these features are supplied into an Attention-based Efficient Bidirectional Network (ImCfO_Attn_EffBNet) based on Improved Crayfish Optimization. EfficientNet-B7, BiLSTM, and Attention modules are integrated by ImCfO_Attn_EffBNet to effectively gather temporal and geographic data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Additionally, we use the Improved Crayfish Optimization (ImCfO) algorithm to maximize convergence rates, optimize the loss function, and find the global best solutions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ImCfO enhances performance by adding a self-adaptation criterion to the traditional crayfish algorithm. The classifier's configurable parameters are adjusted using the ImCfO resultant solution, which raises the prediction accuracy overall.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Based on a number of assessments, the ImCfO_Attn_EffBNet analyzed the performance and found that the results were as follows: accuracy (95.068%), recall (92.948%), specificity (92.89%), F-Score (92.89%), precision (92.89%), and FPR (2.1%), in that order.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329241296352","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T03:49:26Z","timestamp":1733975366000},"page":"1021-1037","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Parkinson's disease prediction using improved crayfish optimization based hybrid deep learning"],"prefix":"10.1177","volume":"33","author":[{"given":"A","family":"Malathi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Anand Institute of Higher Technology, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R","family":"Ramalakshmi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vaibhav","family":"Gandhi","sequence":"additional","affiliation":[{"name":"Department of Design Engineering and Mathematics, Middlesex University, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Bhuvanesh","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2023.01.007"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23042085"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/03091902.2022.2097326"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3284944"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-022-04342-6"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomimetics8040351"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3235040"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2023.3250355"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104147"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2023.101352"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-41314-y"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2023.03.011"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.24018\/ejece.2023.7.2.488"},{"key":"e_1_3_3_15_2","first-page":"1","article-title":"Parkinson\u2019s disease detection and classification using EEG based on deep CNN-LSTM model","author":"Li K","unstructured":"Li K, Ao B, Wu X, et\u00a0al. Parkinson\u2019s disease detection and classification using EEG based on deep CNN-LSTM model. Biotechnol Genet Eng Rev. 1\u201320. DOI: https:\/\/doi.org\/10.1080\/02648725.2023.2200333","journal-title":"Biotechnol Genet Eng Rev."},{"key":"e_1_3_3_16_2","first-page":"2074","article-title":"Intelligent diagnostic prediction and classification system for Parkinson's disease by incorporating Sperm Swarm Optimization (SSO) and density- based feature selection methods","volume":"15","author":"Shehadeh H","year":"2023","unstructured":"Shehadeh H, Jebril I, Jaradat G, et\u00a0al. Intelligent diagnostic prediction and classification system for Parkinson's disease by incorporating Sperm Swarm Optimization (SSO) and density- based feature selection methods. Int J Adv Soft Comput Applications 2023; 15: 2074\u20138523.","journal-title":"Int J Adv Soft Comput Applications"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13061088"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2023.109657"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10050531"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0286506"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16940-3"},{"key":"e_1_3_3_22_2","article-title":"An ensemble of ResNet model for classification of Parkinson disease","volume":"14","author":"Mahendran M","year":"2024","unstructured":"Mahendran M, Visalakshi R. An ensemble of ResNet model for classification of Parkinson disease. Int J Nutr, Pharmacol, Neurol Dis. 2024; 14. DOI: https:\/\/doi.org\/10.4103\/ijnpnd.ijnpnd_22_23","journal-title":"Int J Nutr, Pharmacol, Neurol Dis."},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-09516-1"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-09569-2"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2023.100381"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1080\/00051144.2023.2284030"},{"key":"e_1_3_3_27_2","volume-title":"Parkison diseases EEG dataset","author":"Warner","year":"2023","unstructured":"Warner. To Detecting Parkinson\u2019s Disease \u2013 Python Machine Learning Project. In: Kaggle (ed.) Parkison diseases EEG dataset. California, USA: Kaggle, 2023. 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