{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:48:57Z","timestamp":1778168937833,"version":"3.51.4"},"reference-count":20,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"vor","delay-in-days":111,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011665","name":"Deanship of Scientific Research, King Saud University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data\u2010driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP\u2010SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP\u2010SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model\u2019s effectiveness and efficiency in predicting diabetes disease from the required data attributes.<\/jats:p>","DOI":"10.1155\/2021\/5525271","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T17:35:18Z","timestamp":1619112918000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":117,"title":["An Improved Artificial Neural Network Model for Effective Diabetes Prediction"],"prefix":"10.1155","volume":"2021","author":[{"given":"Muhammad Mazhar","family":"Bukhari","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7479-7102","authenticated-orcid":false,"given":"Bader Fahad","family":"Alkhamees","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1523-1330","authenticated-orcid":false,"given":"Saddam","family":"Hussain","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8512-9687","authenticated-orcid":false,"given":"Abdu","family":"Gumaei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3678-0956","authenticated-orcid":false,"given":"Adel","family":"Assiri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5406-0389","authenticated-orcid":false,"given":"Syed Sajid","family":"Ullah","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,22]]},"reference":[{"key":"e_1_2_9_1_2","unstructured":"SaponM. 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