{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:59:56Z","timestamp":1775473196890,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Appendicitis is a condition wherein the appendix becomes inflamed, and it can be difficult to diagnose accurately. The type of appendicitis can also be hard to determine, leading to misdiagnosis and difficulty in managing the condition. To avoid complications and reduce mortality, early diagnosis and treatment are crucial. While Alvarado\u2019s clinical scoring system is not sufficient, ultrasound and computed tomography (CT) imaging are effective but have downsides such as operator-dependency and radiation exposure. This study proposes the use of machine learning methods and a locally collected reliable dataset to enhance the identification of acute appendicitis while detecting the differences between complicated and non-complicated appendicitis. Machine learning can help reduce diagnostic errors and improve treatment decisions. This study conducted four different experiments using various ML algorithms, including K-nearest neighbors (KNN), DT, bagging, and stacking. The experimental results showed that the stacking model had the highest training accuracy, test set accuracy, precision, and F1 score, which were 97.51%, 92.63%, 95.29%, and 92.04%, respectively. Feature importance and explainable AI (XAI) identified neutrophils, WBC_Count, Total_LOS, P_O_LOS, and Symptoms_Days as the principal features that significantly affected the performance of the model. Based on the outcomes and feedback from medical health professionals, the scheme is promising in terms of its effectiveness in diagnosing of acute appendicitis.<\/jats:p>","DOI":"10.3390\/bdcc8090108","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T09:57:47Z","timestamp":1725443867000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Appendicitis Diagnosis: Ensemble Machine Learning and Explainable Artificial Intelligence-Based Comprehensive Approach"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7521-5757","authenticated-orcid":false,"given":"Mohammed","family":"Gollapalli","sequence":"first","affiliation":[{"name":"Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6696-277X","authenticated-orcid":false,"given":"Atta","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4519-1376","authenticated-orcid":false,"given":"Sheriff A.","family":"Kudos","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Mohammed S.","family":"Foula","sequence":"additional","affiliation":[{"name":"Department of Surgery, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Abdullah Mahmoud","family":"Alkhalifa","sequence":"additional","affiliation":[{"name":"Department of Surgery, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Hassan Mohammed","family":"Albisher","sequence":"additional","affiliation":[{"name":"Department of Surgery, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9807-4870","authenticated-orcid":false,"given":"Mohammed Taha","family":"Al-Hariri","sequence":"additional","affiliation":[{"name":"Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3580-5960","authenticated-orcid":false,"given":"Nazeeruddin","family":"Mohammad","sequence":"additional","affiliation":[{"name":"Cybersecurity Center, Prince Mohammad Bin Fahd University, P.O. Box 1664, Alkhobar 31952, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"ref_1","unstructured":"Lotfollahzadeh, S., Lopez, R.A., and Deppen, J.G. (2022, December 11). Appendicitis, StatPearls Publishing, Available online: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK493193\/."},{"key":"ref_2","unstructured":"(2022, December 11). Mayo Clinic. Appendicitis. Available online: https:\/\/www.mayoclinic.org\/diseases-conditions\/appendicitis\/symptoms-causes\/syc-20369543."},{"key":"ref_3","unstructured":"(2022, November 12). Johns Hopkins Medicine. Appendicitis. 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