{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:49:11Z","timestamp":1780487351835,"version":"3.54.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Altinbas University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the PIMA Indian Diabetes dataset. Patients with diabetes were classified on the basis of laboratory findings. Pre-processing tasks, such as the handling of imbalanced data and missing values, were performed prior to feature importance and normalisation activities. The algorithm with the best performance was examined using precision, accuracy, F1, recall and area under the curve metrics. Then, all ML models were hyper parametrically tuned via grid search to optimise their performance and reduce their error times. The decision process was also evaluated to further enhance the models. The AdaBoost-ET model performed even when features were not selected for binary classification. The model proposed in this study can predict diabetes with unprecedented high accuracy compared with the models in previous studies.<\/jats:p>","DOI":"10.1007\/s11227-024-06082-0","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T06:01:46Z","timestamp":1712383306000},"page":"15664-15689","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model"],"prefix":"10.1007","volume":"80","author":[{"given":"Abdulrahman Ahmed","family":"Jasim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Layth Rafea","family":"Hazim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayder","family":"Mohammedqasim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roa\u2019a","family":"Mohammedqasem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oguz","family":"Ata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar Hussein","family":"Salman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,6]]},"reference":[{"key":"6082_CR1","first-page":"11","volume":"978","author":"WHO Global Report","year":"2016","unstructured":"WHO Global Report (2016) Global report on diabetes. Isbn 978:11","journal-title":"Isbn"},{"key":"6082_CR2","unstructured":"\u201c2013\u20132020 GLOBAL ACTION PLAN FOR THE PREVENTION AND CONTROL OF NONCOMMUNICABLE DISEASES,\u201d 2013, Accessed: Feb. 18, 2023. [Online]. Available: www.who.int"},{"key":"6082_CR3","doi-asserted-by":"publisher","unstructured":"Kaur P, Sharma N, Singh A, Gill B (2019) CI-DPF: a Cloud IoT based framework for diabetes prediction. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, pp 654\u2013660.https:\/\/doi.org\/10.1109\/IEMCON.2018.8614775","DOI":"10.1109\/IEMCON.2018.8614775"},{"issue":"1084","key":"6082_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1136\/POSTGRADMEDJ-2015-133281","volume":"92","author":"F Zaccardi","year":"2016","unstructured":"Zaccardi F, Webb DR, Yates T, Davies MJ (2016) Pathophysiology of type 1 and type 2 diabetes mellitus: a 90-year perspective. Postgrad Med J 92(1084):63\u201369. https:\/\/doi.org\/10.1136\/POSTGRADMEDJ-2015-133281","journal-title":"Postgrad Med J"},{"key":"6082_CR5","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/978-981-16-2164-2_19\/COVER","volume":"218","author":"P Palimkar","year":"2022","unstructured":"Palimkar P, Shaw RN, Ghosh A (2022) Machine learning technique to prognosis diabetes disease: random forest classifier approach. Lect Notes Netw Syst 218:219\u2013244. https:\/\/doi.org\/10.1007\/978-981-16-2164-2_19\/COVER","journal-title":"Lect Notes Netw Syst"},{"key":"6082_CR6","doi-asserted-by":"publisher","first-page":"103693","DOI":"10.1016\/J.JBI.2021.103693","volume":"115","author":"J Li","year":"2021","unstructured":"Li J et al (2021) A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. J Biomed Inform 115:103693. https:\/\/doi.org\/10.1016\/J.JBI.2021.103693","journal-title":"J Biomed Inform"},{"issue":"3","key":"6082_CR7","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.3390\/APP11031173","volume":"11","author":"HF Ahmad","year":"2021","unstructured":"Ahmad HF, Mukhtar H, Alaqail H, Seliaman M, Alhumam A (2021) Investigating health-related features and their impact on the prediction of diabetes using machine learning. Appl Sci 11(3):1173. https:\/\/doi.org\/10.3390\/APP11031173","journal-title":"Appl Sci"},{"key":"6082_CR8","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/J.PROCS.2020.07.058","volume":"175","author":"M Ul Alam","year":"2020","unstructured":"Ul Alam M, Rahmani R (2020) Intelligent context-based healthcare metadata aggregator in internet of medical things platform. Proc Comput Sci 175:411\u2013418. https:\/\/doi.org\/10.1016\/J.PROCS.2020.07.058","journal-title":"Proc Comput Sci"},{"key":"6082_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6632599","author":"B Pradhan","year":"2021","unstructured":"Pradhan B, Bhattacharyya S, Pal K (2021) IoT-based applications in healthcare devices. J Healthc Eng. https:\/\/doi.org\/10.1155\/2021\/6632599","journal-title":"J Healthc Eng"},{"key":"6082_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07049-z","author":"V Chang","year":"2022","unstructured":"Chang V, Bailey J, Xu QA, Sun Z (2022) Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07049-z","journal-title":"Neural Comput Appl"},{"key":"6082_CR11","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1485","author":"T Shaik","year":"2023","unstructured":"Shaik T et al (2023) Remote patient monitoring using artificial intelligence: current state, applications, and challenges. Wiley Interdiscip Rev Data Min Knowl Discov. https:\/\/doi.org\/10.1002\/widm.1485","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"21","key":"6082_CR12","doi-asserted-by":"publisher","first-page":"4604","DOI":"10.3390\/APP9214604","volume":"9","author":"S Larabi-Marie-Sainte","year":"2019","unstructured":"Larabi-Marie-Sainte S, Aburahmah L, Almohaini R, Saba T (2019) Current techniques for diabetes prediction: review and case study. Appl Sci 9(21):4604. https:\/\/doi.org\/10.3390\/APP9214604","journal-title":"Appl Sci"},{"issue":"4","key":"6082_CR13","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1016\/j.jcot.2020.05.011","volume":"11","author":"R Pratap Singh","year":"2020","unstructured":"Pratap Singh R, Javaid M, Haleem A, Vaishya R, Ali S (2020) Internet of medical things (IoMT) for orthopaedic in COVID-19 pandemic: roles, challenges, and applications. J Clin Orthop Trauma 11(4):713\u2013717. https:\/\/doi.org\/10.1016\/j.jcot.2020.05.011","journal-title":"J Clin Orthop Trauma"},{"key":"6082_CR14","doi-asserted-by":"publisher","first-page":"107112","DOI":"10.1109\/ACCESS.2020.3000322","volume":"8","author":"IV Pustokhina","year":"2020","unstructured":"Pustokhina IV, Pustokhin DA, Gupta D, Khanna A, Shankar K, Nguyen GN (2020) An effective training scheme for deep neural network in edge computing enabled internet of medical things (IoMT) systems. IEEE Access 8:107112\u2013107123. https:\/\/doi.org\/10.1109\/ACCESS.2020.3000322","journal-title":"IEEE Access"},{"key":"6082_CR15","doi-asserted-by":"publisher","first-page":"100123","DOI":"10.1016\/J.IOT.2019.100123","volume":"8","author":"F Alsubaei","year":"2019","unstructured":"Alsubaei F, Abuhussein A, Shandilya V, Shiva S (2019) IoMT-SAF: internet of medical things security assessment framework. Internet Things 8:100123. https:\/\/doi.org\/10.1016\/J.IOT.2019.100123","journal-title":"Internet Things"},{"key":"6082_CR16","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.matcom.2022.03.003","volume":"198","author":"A Rajagopal","year":"2022","unstructured":"Rajagopal A, Jha S, Alagarsamy R, Quek SG, Selvachandran G (2022) A novel hybrid machine learning framework for the prediction of diabetes with context-customized regularization and prediction procedures. Math Comput Simul 198:388\u2013406. https:\/\/doi.org\/10.1016\/j.matcom.2022.03.003","journal-title":"Math Comput Simul"},{"key":"6082_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/app12030950","author":"H Salem","year":"2022","unstructured":"Salem H, Shams MY, Elzeki OM, Elfattah MA, Al-amri JF, Elnazer S (2022) Fine-tuning fuzzy KNN classifier based on uncertainty membership for the medical diagnosis of diabetes. Appl Sci. https:\/\/doi.org\/10.3390\/app12030950","journal-title":"Appl Sci"},{"issue":"4","key":"6082_CR18","doi-asserted-by":"publisher","first-page":"6221","DOI":"10.1007\/s11042-022-13582-9","volume":"82","author":"M Shrestha","year":"2023","unstructured":"Shrestha M et al (2023) A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes. Multimed Tools Appl 82(4):6221\u20136241. https:\/\/doi.org\/10.1007\/s11042-022-13582-9","journal-title":"Multimed Tools Appl"},{"key":"6082_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/s22197268","author":"HB Kibria","year":"2022","unstructured":"Kibria HB, Nahiduzzaman M, Goni MOF, Ahsan M, Haider J (2022) An ensemble approach for the prediction of diabetes mellitus using a soft voting classifier with an explainable AI. Sensors. https:\/\/doi.org\/10.3390\/s22197268","journal-title":"Sensors"},{"key":"6082_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104381","author":"Y Su","year":"2023","unstructured":"Su Y, Huang C, Yin W, Lyu X, Ma L, Tao Z (2023) Diabetes Mellitus risk prediction using age adaptation models. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2022.104381","journal-title":"Biomed Signal Process Control"},{"key":"6082_CR21","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.procs.2022.12.107","volume":"216","author":"ME Febrian","year":"2023","unstructured":"Febrian ME, Ferdinan FX, Sendani GP, Suryanigrum KM, Yunanda R (2023) Diabetes prediction using supervised machine learning. Proc Comput Sci 216:21\u201330. https:\/\/doi.org\/10.1016\/j.procs.2022.12.107","journal-title":"Proc Comput Sci"},{"key":"6082_CR22","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1016\/j.procs.2023.01.104","volume":"218","author":"SC Gupta","year":"2023","unstructured":"Gupta SC, Goel N (2023) Predictive modeling and analytics for diabetes using hyperparameter tuned machine learning techniques. Proc Comput Sci 218:1257\u20131269. https:\/\/doi.org\/10.1016\/j.procs.2023.01.104","journal-title":"Proc Comput Sci"},{"issue":"4","key":"6082_CR23","doi-asserted-by":"publisher","first-page":"2344","DOI":"10.3390\/app13042344","volume":"13","author":"K Al Sadi","year":"2023","unstructured":"Al Sadi K, Balachandran W (2023) Prediction model of Type 2 diabetes mellitus for oman prediabetes patients using artificial neural network and six machine learning classifiers. Appl Sci 13(4):2344. https:\/\/doi.org\/10.3390\/app13042344","journal-title":"Appl Sci"},{"key":"6082_CR24","unstructured":"\u201cPima Indians Diabetes Database | Kaggle.\u201d Accessed: Feb. 24, 2023. [Online]. Available: https:\/\/www.kaggle.com\/datasets\/uciml\/pima-indians-diabetes-database"},{"issue":"12","key":"6082_CR25","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.3390\/MEDICINA58121745","volume":"58","author":"H Mohammedqasim","year":"2022","unstructured":"Mohammedqasim H, Mohammedqasem R, Ata O, Alyasin EI (2022) Diagnosing coronary artery disease on the basis of hard ensemble voting optimization. Medicina 58(12):1745. https:\/\/doi.org\/10.3390\/MEDICINA58121745","journal-title":"Medicina"},{"key":"6082_CR26","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/J.INS.2019.07.070","volume":"505","author":"D Elreedy","year":"2019","unstructured":"Elreedy D, Atiya AF (2019) A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance. Inf Sci (N Y) 505:32\u201364. https:\/\/doi.org\/10.1016\/J.INS.2019.07.070","journal-title":"Inf Sci (N Y)"},{"issue":"11","key":"6082_CR27","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.3390\/MEDICINA57111217","volume":"57","author":"HM Qasim","year":"2021","unstructured":"Qasim HM, Ata O, Ansari MA, Alomary MN, Alghamdi S, Almehmadi M (2021) Hybrid feature selection framework for the Parkinson imbalanced dataset prediction problem. Medicina 57(11):1217. https:\/\/doi.org\/10.3390\/MEDICINA57111217","journal-title":"Medicina"},{"key":"6082_CR28","doi-asserted-by":"publisher","first-page":"103763","DOI":"10.1016\/J.JBI.2021.103763","volume":"117","author":"JL Speiser","year":"2021","unstructured":"Speiser JL (2021) A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data. J Biomed Inform 117:103763. https:\/\/doi.org\/10.1016\/J.JBI.2021.103763","journal-title":"J Biomed Inform"},{"issue":"2","key":"6082_CR29","doi-asserted-by":"publisher","first-page":"614","DOI":"10.3390\/S21020614","volume":"21","author":"L Borz\u00ec","year":"2021","unstructured":"Borz\u00ec L, Mazzetta I, Zampogna A, Suppa A, Olmo G, Irrera F (2021) Prediction of freezing of gait in Parkinson\u2019s disease using wearables and machine learning. Sensors 21(2):614. https:\/\/doi.org\/10.3390\/S21020614","journal-title":"Sensors"},{"issue":"5","key":"6082_CR30","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.3390\/DIAGNOSTICS12051023","volume":"12","author":"K Debjit","year":"2022","unstructured":"Debjit K et al (2022) An improved machine-learning approach for COVID-19 prediction using harris hawks optimization and feature analysis using SHAP. Diagnostics 12(5):1023. https:\/\/doi.org\/10.3390\/DIAGNOSTICS12051023","journal-title":"Diagnostics"},{"issue":"2","key":"6082_CR31","doi-asserted-by":"publisher","first-page":"354","DOI":"10.3390\/DIAGNOSTICS11020354","volume":"11","author":"OS T\u0103taru","year":"2021","unstructured":"T\u0103taru OS et al (2021) Artificial intelligence and machine learning in prostate cancer patient management\u2014current trends and future perspectives. Diagnostics 11(2):354. https:\/\/doi.org\/10.3390\/DIAGNOSTICS11020354","journal-title":"Diagnostics"},{"key":"6082_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/S00521-022-07049-Z\/FIGURES\/17","author":"V Chang","year":"2022","unstructured":"Chang V, Bailey J, Xu QA, Sun Z (2022) Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Comput Appl. https:\/\/doi.org\/10.1007\/S00521-022-07049-Z\/FIGURES\/17","journal-title":"Neural Comput Appl"},{"key":"6082_CR33","doi-asserted-by":"publisher","first-page":"3571","DOI":"10.1016\/J.MATPR.2021.11.635","volume":"56","author":"J Singh-Kushwah","year":"2022","unstructured":"Singh-Kushwah J, Kumar A, Patel S, Soni R, Gawande A, Gupta S (2022) Comparative study of regressor and classifier with decision tree using modern tools. Mater Today Proc 56:3571\u20133576. https:\/\/doi.org\/10.1016\/J.MATPR.2021.11.635","journal-title":"Mater Today Proc"},{"issue":"9","key":"6082_CR34","doi-asserted-by":"publisher","first-page":"136","DOI":"10.3390\/COMPUTERS11090136","volume":"11","author":"G Alfian","year":"2022","unstructured":"Alfian G et al (2022) Predicting breast cancer from risk factors using SVM and extra-trees-based feature selection method. Computers 11(9):136. https:\/\/doi.org\/10.3390\/COMPUTERS11090136","journal-title":"Computers"},{"key":"6082_CR35","doi-asserted-by":"publisher","first-page":"100950","DOI":"10.1016\/J.JOBE.2019.100950","volume":"27","author":"M Gong","year":"2020","unstructured":"Gong M, Bai Y, Qin J, Wang J, Yang P, Wang S (2020) Gradient boosting machine for predicting return temperature of district heating system: a case study for residential buildings in Tianjin. J Build Eng 27:100950. https:\/\/doi.org\/10.1016\/J.JOBE.2019.100950","journal-title":"J Build Eng"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06082-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06082-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06082-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T11:28:30Z","timestamp":1719314910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06082-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,6]]},"references-count":35,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["6082"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06082-0","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,6]]},"assertion":[{"value":"18 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This is not applicable for this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}