{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:45:24Z","timestamp":1760060724589,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program Management Unit for Human Resources &amp; Institutional Development, Research and Innovation (PMU-B)","award":["B04G640071","BG-RRP-2.013-0001"],"award-info":[{"award-number":["B04G640071","BG-RRP-2.013-0001"]}]},{"name":"European Union (EU) NextGenerationEU","award":["B04G640071","BG-RRP-2.013-0001"],"award-info":[{"award-number":["B04G640071","BG-RRP-2.013-0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Dementia and heart failure are growing global health issues, exacerbated by aging populations and disparities in care access. Diagnosing these conditions often requires advanced equipment or tests with limited availability. A reliable tool distinguishing between the two conditions is essential, enabling more accurate diagnoses and reducing misclassifications and inappropriate referrals. This study proposes a novel measurement, the optimized weighted objective distance (OWOD), a modified version of the weighted objective distance, for the classification of dementia and heart failure. The OWOD is designed to enhance model generalization through a data-driven approach. By enhancing objective class generalization, applying multi-feature distance normalization, and identifying the most significant features for classification\u2014together with newly integrated blood biomarker features\u2014the OWOD could strengthen the classification of dementia and heart failure. A combination of risk factors and proposed blood biomarkers (derived from 10,000 electronic health records at Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand), comprising 20 features, demonstrated the best OWOD classification performance. For model evaluation, the proposed OWOD-based classification method attained an accuracy of 95.45%, a precision of 96.14%, a recall of 94.70%, an F1-score of 95.42%, and an area under the receiver operating characteristic curve of 97.10%, surpassing the results obtained using other machine learning-based classification models (gradient boosting, decision tree, neural network, and support vector machine).<\/jats:p>","DOI":"10.3390\/bioengineering12090980","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T08:56:46Z","timestamp":1758013006000},"page":"980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dementia and Heart Failure Classification Using Optimized Weighted Objective Distance and Blood Biomarker-Based Features"],"prefix":"10.3390","volume":"12","author":[{"given":"Veerasak","family":"Noonpan","sequence":"first","affiliation":[{"name":"Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai 57100, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Supansa","family":"Chaising","sequence":"additional","affiliation":[{"name":"School of Management, Mae Fah Luang University, Chiang Rai 57100, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3216-3870","authenticated-orcid":false,"given":"Georgi","family":"Hristov","sequence":"additional","affiliation":[{"name":"Telecommunications Department, University of Ruse, 7017 Ruse, Bulgaria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9847-157X","authenticated-orcid":false,"given":"Punnarumol","family":"Temdee","sequence":"additional","affiliation":[{"name":"Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai 57100, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/S0140-6736(24)01296-0","article-title":"Dementia prevention, intervention, and care: 2024 report of the Lancet standing commission","volume":"404","author":"Livingston","year":"2024","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1038\/s43856-025-00938-z","article-title":"The role of life\u2019s crucial 9 in cardiovascular disease incidence and dynamic transitions to dementia","volume":"5","author":"Zhang","year":"2025","journal-title":"Commun. Med."},{"key":"ref_3","first-page":"303","article-title":"Cognitive dysfunction in heart failure: Pathophysiology and implications for patient management","volume":"19","author":"Ye","year":"2022","journal-title":"Comorbidities"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, J., Xiao, G., Liang, Y., He, S., Lyu, M., and Zhu, Y. (2024). Heart\u2013brain interaction in cardiogenic dementia: Pathophysiology and therapeutic potential. Front. Cardiovasc. Med., 11.","DOI":"10.3389\/fcvm.2024.1304864"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1161\/CIRCULATIONAHA.123.067626","article-title":"Development and validation of the American Heart Association\u2019s PREVENT equations","volume":"149","author":"Khan","year":"2024","journal-title":"Circulation"},{"key":"ref_6","unstructured":"World Health Organization (2025, September 08). Dementia. WHO Fact Sheets. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia."},{"key":"ref_7","unstructured":"GBD 2019 Dementia Forecasting Collaborators (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health, 7, e105\u2013e125."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104413","DOI":"10.1016\/j.ijnurstu.2022.104413","article-title":"Digital healthcare for dementia and cognitive impairment: A scoping review","volume":"140","author":"Sohn","year":"2023","journal-title":"Int. J. Nurs. Stud."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Stoppa, E., Di Donato, G.W., Parde, N., and Santambrogio, M.D. (2022, January 24\u201326). Computer-aided dementia detection: How informative are your features? . Proceedings of the IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI), Paris, France.","DOI":"10.1109\/RTSI55261.2022.9905097"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chaiyo, Y., and Temdee, P. (2022, January 26\u201328). A comparison of machine learning methods with feature extraction for classification of patients with dementia risk. Proceedings of the Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Chiang Rai, Thailand.","DOI":"10.1109\/ECTIDAMTNCON53731.2022.9720424"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hu, S., Yang, C., and Luo, H. (2022). Current trends in blood biomarker detection and imaging for Alzheimer\u2019s disease. Biosens. Bioelectron., 210.","DOI":"10.1016\/j.bios.2022.114278"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3272","DOI":"10.1093\/cvr\/cvac013","article-title":"Global burden of heart failure: A comprehensive and updated review of epidemiology","volume":"118","author":"Savarese","year":"2022","journal-title":"Cardiovasc. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, X., Lawler, K., Garg, S., Bai, Q., and Alty, J. (2022). Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions. J. Biomed. Inform., 127.","DOI":"10.1016\/j.jbi.2022.104030"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mahendran, N., and Durai Raj Vincent, P.M. (2022). A deep learning framework with an embedded-based feature selection approach for the early detection of Alzheimer\u2019s disease. Comput. Biol. Med., 141.","DOI":"10.1016\/j.compbiomed.2021.105056"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ahmed, H., Soliman, H., and Elmogy, M. (2022). Early detection of Alzheimer\u2019s disease using single nucleotide polymorphisms analysis based on gradient boosting tree. Comput. Biol. Med., 146.","DOI":"10.1016\/j.compbiomed.2022.105622"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"155.e5","DOI":"10.1016\/j.neurobiolaging.2019.05.009","article-title":"Analysis of dementia-related gene variants in APOE \u03b54 noncarrying Korean patients with early-onset Alzheimer\u2019s disease","volume":"85","author":"Park","year":"2020","journal-title":"Neurobiol. Aging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2022.09.009","article-title":"Automatic detection of Alzheimer\u2019s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers","volume":"512","author":"Ali","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shanmugam, J.V., Duraisamy, B., Simon, B.C., and Bhaskaran, P. (2022). Alzheimer\u2019s disease classification using pre-trained deep networks. Biomed. Signal Process. Control, 71.","DOI":"10.1016\/j.bspc.2021.103217"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Di Benedetto, M., Carrara, F., Tafuri, B., Nigro, S., De Blasi, R., Falchi, F., Gennaro, C., Gigli, G., Logroscino, G., and Amato, G. (2022). Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources. Comput. Biol. Med., 148.","DOI":"10.1016\/j.compbiomed.2022.105937"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rallabandi, V.P.S., and Seetharaman, K. (2023). Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer\u2019s disease using fusion of MRI-PET imaging. Biomed. Signal Process. Control, 80.","DOI":"10.1016\/j.bspc.2022.104312"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107562","DOI":"10.1109\/ACCESS.2020.3001149","article-title":"heart disease identification method using machine learning classification in e-healthcare","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ahmad, A.A., and Polat, H. (2023). Prediction of heart disease based on machine learning using jellyfish optimization algorithm. Diagnostics, 13.","DOI":"10.3390\/diagnostics13142392"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Aslam, M.U., Xu, S., Hussain, S., Waqas, M., and Abiodun, N.L. (2024). Machine learning based classification of valvular heart disease using cardiovascular risk factors. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-67973-z"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chulde-Fern\u00e1ndez, B., Enr\u00edquez-Ortega, D., Tirado-Esp\u00edn, A., Vizca\u00edno-Imaca\u00f1a, P., Guevara, C., Navas, P., Villalba-Meneses, F., Cadena-Morejon, C., Almeida-Galarraga, D., and Acosta-Vargas, P. (2025). Classification of heart failure using machine learning: A comparative study. Life, 15.","DOI":"10.3390\/life15030496"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"48677","DOI":"10.1109\/ACCESS.2023.3276468","article-title":"Gradient boosting-based model for elderly heart failure, aortic stenosis, and dementia classification","volume":"11","author":"Yongcharoenchaiyasit","year":"2023","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yongcharoenchaiyasit, K., Arwatchananukul, S., Hristov, G., and Temdee, P. (2025). Enhanced multi-model machine learning-based dementia detection using a data enrichment framework: Leveraging the blessing of dimensionality. Bioengineering, 12.","DOI":"10.3390\/bioengineering12060592"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106441","DOI":"10.1016\/j.knosys.2020.106441","article-title":"Weighted objective distance for the classification of elderly people with hypertension","volume":"210","author":"Chaising","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"49018","DOI":"10.1109\/ACCESS.2021.3117269","article-title":"Average weighted objective distance-based method for type 2 diabetes prediction","volume":"9","author":"Nuankaew","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.semcdb.2021.03.002","article-title":"Hypertension-related risk for dementia: A summary review with future directions","volume":"116","author":"Daugherty","year":"2021","journal-title":"Semin. Cell Dev. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"S318","DOI":"10.1016\/j.jval.2022.04.173","article-title":"CO76 Hypertension and the risk of dementia among medicare beneficiaries in the U.S","volume":"25","author":"Brown","year":"2022","journal-title":"Value Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jns.2019.10.1050","article-title":"Dementia risk and hypertension: A literature review","volume":"405","author":"Campbell","year":"2019","journal-title":"J. Neurol. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110015","DOI":"10.1016\/j.diabres.2022.110015","article-title":"Body weight variability and the risk of dementia in patients with type 2 diabetes mellitus: A nationwide cohort study in Korea","volume":"190","author":"Park","year":"2022","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e498","DOI":"10.1016\/S2666-7568(21)00150-1","article-title":"Blood cholesterol and risk of dementia in more than 1.8 million people over two decades: A retrospective cohort study","volume":"2","author":"Iwagami","year":"2021","journal-title":"Lancet Healthy Longev."},{"key":"ref_34","first-page":"101695","article-title":"Serum lipid traits and the risk of dementia: A cohort study of 254,575 women and 214,891 men in the UK Biobank","volume":"54","author":"Gong","year":"2022","journal-title":"Lancet"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101298","DOI":"10.1016\/j.csl.2021.101298","article-title":"An automatic Alzheimer\u2019s disease classifier based on spontaneous spoken English","volume":"72","author":"Bertini","year":"2022","journal-title":"Comput. Speech Lang."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Milana, S. (2023, January 19\u201321). Dementia classification using attention mechanism on audio data. Proceedings of the 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl\u2019any, Slovakia.","DOI":"10.1109\/SAMI58000.2023.10044539"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104236","DOI":"10.1016\/j.ijnurstu.2022.104236","article-title":"Multidomain lifestyle interventions for cognition and the risk of dementia: A systematic review and meta-analysis","volume":"130","author":"Meng","year":"2022","journal-title":"Int. J. Nurs. Stud."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104822","DOI":"10.1016\/j.engappai.2022.104822","article-title":"Diagnosing Alzheimer\u2019s disease from on-line handwriting: A novel dataset and performance benchmarking","volume":"111","author":"Cilia","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Masuo, A., Ito, Y., Kanaiwa, T., Naito, K., Sakuma, T., and Kato, S. (2022, January 11\u201315). Dementia screening based on SVM using qualitative drawing error of clock drawing test. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9871889"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.amepre.2023.01.008","article-title":"Sleep disturbances and dementia risk in older adults: Findings from 10 years of national U.S. prospective data","volume":"64","author":"Wong","year":"2023","journal-title":"Am. J. Prev. Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Khosroazad, S., Abedi, A., and Hayes, M.J. (2023). Sleep signal analysis for early detection of Alzheimer\u2019s disease and related dementia (ADRD). IEEE J. Biomed. Health Inform., 27.","DOI":"10.1109\/JBHI.2023.3235391"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105285","DOI":"10.1016\/j.parkreldis.2023.105285","article-title":"Sex differences in dementia with Lewy bodies: Focused review of available evidence and future directions","volume":"107","author":"Chiu","year":"2023","journal-title":"Park. Relat. Disord."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108815","DOI":"10.1016\/j.knosys.2022.108815","article-title":"A deep learning based framework for diagnosis of mild cognitive impairment","volume":"248","author":"Alvi","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ypmed.2018.04.004","article-title":"Development of a risk score for the prediction of incident dementia in older adults using a frailty index and health checkup data: The JAGES longitudinal study","volume":"112","author":"Lin","year":"2018","journal-title":"Prev. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.jad.2022.07.008","article-title":"Modifiable risk factors for incident dementia and cognitive impairment: An umbrella review of evidence","volume":"314","author":"Zhang","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100709","DOI":"10.1016\/j.ajpc.2024.100709","article-title":"Sex-related differences in the 20-year incidence of CVD and its risk factors: The ATTICA study (2002\u20132022)","volume":"19","author":"Sigala","year":"2024","journal-title":"Am. J. Prev. Cardiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.ecl.2022.02.005","article-title":"Assessment of cardiovascular disease risk: A 2022 update","volume":"51","author":"Osuji","year":"2022","journal-title":"Endocrinol. Metab. Clin. N. Am."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e025519","DOI":"10.1161\/JAHA.122.025519","article-title":"Incidence of preclinical heart failure in a community population","volume":"11","author":"Young","year":"2022","journal-title":"J. Am. Heart Assoc."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sevilla-Salcedo, C., Imani, V., Olmos, P.M., G\u00f3mez-Verdejo, V., and Tohka, J. (2022). Multi-task longitudinal forecasting with missing values on Alzheimer\u2019s disease. Comput. Methods Programs Biomed., 226.","DOI":"10.1016\/j.cmpb.2022.107056"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"102074","DOI":"10.1016\/j.compmedimag.2022.102074","article-title":"Attention-guided neural network for early dementia detection using MRS data","volume":"99","author":"Kherchouche","year":"2022","journal-title":"Comput. Med. Imag. Graph."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ahmad, M., Ahmed, A., Hashim, H., Farsi, M., and Mahmoud, N. (2025). Enhancing heart disease diagnosis using ECG signal reconstruction and deep transfer learning classification with optional SVM integration. Diagnostics, 15.","DOI":"10.3390\/diagnostics15121501"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"170212","DOI":"10.1016\/j.ijleo.2022.170212","article-title":"Automated prediction system for Alzheimer detection based on deep residual autoencoder and support vector machine","volume":"272","author":"Menagadevi","year":"2023","journal-title":"Optik"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"100441","DOI":"10.1016\/j.imu.2020.100441","article-title":"Congestive heart failure waveform classification based on short time-step analysis with recurrent network","volume":"21","author":"Darmawahyuni","year":"2020","journal-title":"Informat. Med. Unlocked"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chaiyo, Y., Rueangsirarak, W., Hristov, G., and Temdee, P. (2025). Improving early detection of dementia: Extra trees-based classification model using inter-relation-based features and K-means synthetic minority oversampling technique. Big Data Cogn. Comput., 9.","DOI":"10.3390\/bdcc9060148"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Phanbua, P., Arwatchananukul, S., and Temdee, P. (February, January 29). Classification model of dementia and heart failure in older adults using extra trees and oversampling-based technique. Proceedings of the 2025 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Nan, Thailand.","DOI":"10.1109\/ECTIDAMTNCON64748.2025.10962096"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Dhar, A., Dash, N., and Roy, K. (2017, January 15\u201316). Classification of text documents through distance measurement: An experiment with multi-domain Bangla text documents. Proceedings of the 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA) (Fall), Dehradun, India.","DOI":"10.1109\/ICACCAF.2017.8344721"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Greche, L., Jazouli, M., Es-Sbai, N., Majda, A., and Zarghili, A. (2017, January 19\u201320). Comparison between Euclidean and Manhattan distance measure for facial expressions classification. Proceedings of the 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco.","DOI":"10.1109\/WITS.2017.7934618"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.aasri.2013.10.009","article-title":"Comparison of distance measures on Fuzzyc-means algorithm for image classification problem","volume":"4","author":"Han","year":"2013","journal-title":"AASRI Procedia"},{"key":"ref_59","first-page":"19","article-title":"Assessment of student competency for personalised online learning using objective distance","volume":"23","author":"Chaichumpa","year":"2018","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chaising, S., and Temdee, P. (2018, January 25\u201328). Determining recommendations for preventing elderly people from cardiovascular disease complication using objective distance. Proceedings of the 2018 Global Wireless Summit (GWS), Chiang Rai, Thailand.","DOI":"10.1109\/GWS.2018.8686527"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11277-019-06639-w","article-title":"Personalized recommendation method for preventing elderly people from cardiovascular disease complication using integrated objective distance","volume":"117","author":"Chaising","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Alhaj, T.A., Siraj, M.M., Zainal, A., Elshoush, H.T., and Elhaj, F. (2016). Feature selection using information gain for improved structural-based alert correlation. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0166017"},{"key":"ref_63","first-page":"48","article-title":"Information gain feature selection for multi-label classification","volume":"6","author":"Pereira","year":"2015","journal-title":"J. Inf. Data Manag."},{"key":"ref_64","first-page":"18","article-title":"Feature selection based on information gain","volume":"2","author":"Azhagusundari","year":"2013","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_65","unstructured":"Patil, L.H., and Atique, M. (2013, January 7\u20139). A novel feature selection based on information gain using WordNet. Proceedings of the 2013 International Conference on Information and Network Security (ICINS 2013), London, UK."},{"key":"ref_66","unstructured":"Baobao, B., Jinsheng, M., and Minru, S. (2008, January 21\u201324). An enhancement of K-Nearest Neighbor algorithm using information gain and extension relativity. Proceedings of the 2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China."},{"key":"ref_67","first-page":"34","article-title":"Dynamic k-NN with attribute weighting for automatic web page classification (Dk-NNwAW)","volume":"58","author":"Gupta","year":"2012","journal-title":"Int. J. Comput. Appl."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/9\/980\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:46:04Z","timestamp":1760035564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/9\/980"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":67,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["bioengineering12090980"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12090980","relation":{},"ISSN":["2306-5354"],"issn-type":[{"type":"electronic","value":"2306-5354"}],"subject":[],"published":{"date-parts":[[2025,9,15]]}}}