e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model
| dc.contributor.author | Jasim, Abdulrahman Ahmed | |
| dc.contributor.author | Hazim, Layth Rafea | |
| dc.contributor.author | Mohammedqasim, Hayder | |
| dc.contributor.author | Mohammedqasem, Roa’a | |
| dc.contributor.author | Ata, Oğuz | |
| dc.contributor.author | Salman, Omar Hussein | |
| dc.date.accessioned | 2024-04-27T12:23:31Z | |
| dc.date.available | 2024-04-27T12:23:31Z | |
| dc.date.issued | 2024 | en_US |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | Jasim, A. A., Hazim, L. R., Mohammedqasim, H., Mohammedqasem, R., Ata, O., Salman, O. H. (2024). e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model. Journal of Supercomputing. 10.1007/s11227-024-06082-0 | en_US |
| dc.identifier.issn | 0920-8542 | |
| dc.identifier.scopus | 2-s2.0-85189330594 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/4672 | |
| dc.identifier.wos | WOS:001197630900001 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Jasim, Abdulrahman Ahmed | |
| dc.institutionauthor | Hazim, Layth Rafea | |
| dc.institutionauthor | Ata, Oğuz | |
| dc.language.iso | en | |
| dc.publisher | Springer | en_US |
| dc.relation.ispartof | Journal of Supercomputing | |
| dc.relation.isversionof | 10.1007/s11227-024-06082-0 | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrenci | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Diabetes disease | en_US |
| dc.subject | e-Diagnostic | en_US |
| dc.subject | Hyper machine learning | en_US |
| dc.subject | IoMT | en_US |
| dc.subject | Telemedicine | en_US |
| dc.title | e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model | |
| dc.type | Article |
