{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:01:53Z","timestamp":1780999313545,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819573936","type":"print"},{"value":"9789819573943","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-7394-3_35","type":"book-chapter","created":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T23:30:25Z","timestamp":1778455825000},"page":"507-521","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Trust in\u00a0AI-Driven Diabetes Prediction: Explainability Through SHAP and\u00a0Counterfactual Analysis"],"prefix":"10.1007","author":[{"given":"Razan","family":"Malluhi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud","family":"Barhamgi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Salem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Qadeib","family":"Alban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Badawy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"35_CR1","unstructured":"Diabetes around the world in 2021. https:\/\/diabetesatlas.org\/"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Albini, E., Long, J., Dervovic, D., Magazzeni, D.: Counterfactual shapley additive explanations. In: ACM International Conference Proceeding Series, vol. 17 (2022)","DOI":"10.1145\/3531146.3533168"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Ali, S., Abuhmed, T., et al.: Explainable artificial intelligence (xai): what we know and what is left to attain trustworthy artificial intelligence. Inf. Fusion 99 (2023)","DOI":"10.1016\/j.inffus.2023.101805"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K., Kujala, S.: Transparency and explainability of ai systems: from ethical guidelines to requirements. Inf. Softw. Technol. 159 (2023)","DOI":"10.1016\/j.infsof.2023.107197"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Carvalho, D.V., Pereira, E.M., Cardoso, J.S.: Machine learning interpretability: a survey on methods and metrics. Electronics 8 (2019)","DOI":"10.3390\/electronics8080832"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Chen, M., Hern\u00e1ndez, A.: Towards an explainable model for sepsis detection based on sensitivity analysis. IRBM 43 (2022)","DOI":"10.1016\/j.irbm.2021.05.006"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Febrian, M.E., Ferdinan, F.X., Sendani, G.P., Suryanigrum, K.M., Yunanda, R.: Diabetes prediction using supervised machine learning. Procedia Comput. Sci. 216 (2023)","DOI":"10.1016\/j.procs.2022.12.107"},{"key":"35_CR8","doi-asserted-by":"crossref","unstructured":"Hasan, M.K., Alam, M.A., Das, D., Hossain, E., Hasan, M.: Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access 8 (2020)","DOI":"10.1109\/ACCESS.2020.2989857"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Hassija, V., et al.: Interpreting black-box models: a review on explainable artificial intelligence. Cogn. Comput. 16 (2024)","DOI":"10.1007\/s12559-023-10179-8"},{"key":"35_CR10","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. (2017)"},{"key":"35_CR11","unstructured":"Malathy, S., Santhiya, M., Vanitha, C.N., Karthiga, R.R.: Diabetes disease prediction using artificial neural network with machine learning approaches. In: Proceedings of the 5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021 (2021)"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Mertes, S., Huber, T., Weitz, K., Heimerl, A., Andr\u00e9, E.: Ganterfactual\u2013counterfactual explanations for medical non-experts using generative adversarial learning. Front. Artif. Intell. 5 (2022)","DOI":"10.3389\/frai.2022.825565"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinf. 19 (2018)","DOI":"10.1093\/bib\/bbx044"},{"key":"35_CR14","unstructured":"Mustafa, M.: Diabetes prediction dataset (2022). https:\/\/www.kaggle.com\/datasets\/iammustafatz\/diabetes-prediction-dataset"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Parimala, G., Kayalvizhi, R., Nithiya, S.: Diabetes prediction using machine learning. In: International Conference on Computer Communication and Informatics (2023)","DOI":"10.1109\/ICCCI56745.2023.10128216"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Praveenraj, D.D.W., et al.: Exploring explainable artificial intelligence for transparent decision making. In: E3S Web of Conferences, vol.\u00a0399. EDP Sciences (2023)","DOI":"10.1051\/e3sconf\/202339904030"},{"key":"35_CR17","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should i trust you?\u201d: explaining the predictions of any classifier. In: KDD Conference (2016)"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Swamy, S., Noor, S.M., Mathew, R.O.: Cardiovascular disease in diabetes and chronic kidney disease. J. Clin. Med. 12 (2023)","DOI":"10.3390\/jcm12226984"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Tanim, S.A., Aurnob, A.R., Shrestha, T.E., Emon, M.R.I., Mridha, M.F., Miah, M.S.U.: Explainable deep learning for diabetes diagnosis with deepnetx2. Biomed. Signal Process. Control 99 (2025)","DOI":"10.1016\/j.bspc.2024.106902"},{"key":"35_CR20","unstructured":"Tanyel, T., Ayvaz, S., Keserci, B.: Beyond known reality: exploiting counterfactual explanations for medical research (2023)"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering - WISE 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7394-3_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T09:41:14Z","timestamp":1780998074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7394-3_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819573936","9789819573943"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7394-3_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakech","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2025.ficloud.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}