{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T07:30:32Z","timestamp":1780990232476,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to confirm the disease and no biomarker with which one can predict the response to treatment and outcome. In this work, we conduct a feasibility study to check if machine learning can be applied effectively for the diagnosis of ITP using routine blood tests and demographic data in a non-acute outpatient setting. Various ML models, including Logistic Regression, Support Vector Machine, k-Nearest Neighbor, Decision Tree and Random Forest, were applied to data from the UK Adult ITP Registry and a general haematology clinic. Two different approaches were investigated: a demographic-unaware and a demographic-aware one. We conduct extensive experiments to evaluate the predictive performance of these models and approaches, as well as their bias. The results revealed that Decision Tree and Random Forest models were both superior and fair, achieving nearly perfect predictive and fairness scores, with platelet count identified as the most significant variable. Models not provided with demographic information performed better in terms of predictive accuracy but showed lower fairness scores, illustrating a trade-off between predictive performance and fairness.<\/jats:p>","DOI":"10.3390\/diagnostics14131352","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T05:03:07Z","timestamp":1719378187000},"page":"1352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3512-8683","authenticated-orcid":false,"given":"Haroon","family":"Miah","sequence":"first","affiliation":[{"name":"Centre of Immunobiology, Blizard Institute, Queen Mary University of London, London E1 2AT, UK"},{"name":"Haematology Department, Barts Health NHS Trust, London E1 1BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8188-3751","authenticated-orcid":false,"given":"Dimitrios","family":"Kollias","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering & Computer Science, Queen Mary University of London, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7727-2766","authenticated-orcid":false,"given":"Giacinto Luca","family":"Pedone","sequence":"additional","affiliation":[{"name":"Haematology Department, Barts Health NHS Trust, London E1 1BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5110-8455","authenticated-orcid":false,"given":"Drew","family":"Provan","sequence":"additional","affiliation":[{"name":"Centre of Immunobiology, Blizard Institute, Queen Mary University of London, London E1 2AT, UK"},{"name":"Haematology Department, Barts Health NHS Trust, London E1 1BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1968-9920","authenticated-orcid":false,"given":"Frederick","family":"Chen","sequence":"additional","affiliation":[{"name":"Centre of Immunobiology, Blizard Institute, Queen Mary University of London, London E1 2AT, UK"},{"name":"Haematology Department, Barts Health NHS Trust, London E1 1BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Provan, D., and Semple, J. (2022). Recent advances in the mechanisms and treatment of immune thrombocytopenia. EBioMedicine, 76.","DOI":"10.1016\/j.ebiom.2022.103820"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1111\/ejh.13803","article-title":"Incidence of adult primary immune thrombocytopenia in England\u2014An update","volume":"109","author":"Doobaree","year":"2022","journal-title":"Eur. J. Haematol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s40747-017-0064-6","article-title":"Deep neural architectures for prediction in healthcare","volume":"4","author":"Kollias","year":"2018","journal-title":"Complex Intell. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.4103\/jfmpc.jfmpc_440_19","article-title":"Overview of artificial intelligence in medicine","volume":"8","author":"Malik","year":"2019","journal-title":"J. Fam. Med. Prim. Care"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108202","DOI":"10.1016\/j.compeleceng.2022.108202","article-title":"A new blockchain and fog computing model for blood pressure medical sensor data storage","volume":"102","author":"Mani","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100119","DOI":"10.1016\/j.tbench.2023.100119","article-title":"CoviDetector: A transfer learning-based semi supervised approach to detect Covid-19 using CXR images","volume":"3","author":"Chowdhury","year":"2023","journal-title":"Benchcouncil Trans. Benchmarks Stand. Eval."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"119105","DOI":"10.1016\/j.eswa.2022.119105","article-title":"Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium","volume":"214","author":"Zhao","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1080\/0952813X.2022.2058097","article-title":"COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients","volume":"36","author":"Iwendi","year":"2024","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1016\/j.scib.2023.08.001","article-title":"A life-threatening bleeding prediction model for immune thrombocytopenia based on personalized machine learning: A nationwide prospective cohort study","volume":"68","author":"An","year":"2023","journal-title":"Sci. Bull."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4320","DOI":"10.1182\/bloodadvances.2022007226","article-title":"Developing and validating a mortality prediction model for ICH in ITP: A nationwide representative multicenter study","volume":"6","author":"Chong","year":"2022","journal-title":"Blood Adv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1182\/blood-2021-153989","article-title":"Predicting Chronic Immune Thrombocytopenia in Pediatric Patients at Disease Presentation: Leveraging Clinical and Laboratory Characteristics Via Machine Learning Models","volume":"138","author":"Kim","year":"2021","journal-title":"Blood"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1182\/blood-2021-148987","article-title":"Machine-Learning Model for Resistance\/Relapse Prediction in Immune Thrombocytopenia Using Gut Microbiota and Function Signatures","volume":"138","author":"Liu","year":"2021","journal-title":"Blood"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Menard, S. (2002). Applied Logistic Regression Analysis, Sage. Number 106.","DOI":"10.4135\/9781412983433"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_16","first-page":"130","article-title":"Decision tree methods: Applications for classification and prediction","volume":"27","author":"Song","year":"2015","journal-title":"Shanghai Arch. Psychiatry"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_18","unstructured":"Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Verma, S., and Rubin, J. (2018, January 29). Fairness definitions explained. Proceedings of the International Workshop on Software Fairness, Gothenburg, Sweden.","DOI":"10.1145\/3194770.3194776"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457607","article-title":"A survey on bias and fairness in machine learning","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_21","unstructured":"Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities, MIT Press."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Garg, P., Villasenor, J., and Foggo, V. (2020, January 10\u201313). Fairness metrics: A comparative analysis. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378025"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","article-title":"Permutation importance: A corrected feature importance measure","volume":"26","author":"Altmann","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105524","DOI":"10.1016\/j.asoc.2019.105524","article-title":"Investigating the impact of data normalization on classification performance","volume":"97","author":"Singh","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_25","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","unstructured":"Kleinberg, J., Mullainathan, S., and Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv."},{"key":"ref_27","first-page":"26230","article-title":"On the tradeoff between robustness and fairness","volume":"35","author":"Ma","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","unstructured":"Menon, A.K., and Williamson, R.C. (2018, January 23\u201324). The cost of fairness in binary classification. Proceedings of the Conference on Fairness, Accountability and Transparency, New York, NY, USA."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/13\/1352\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:04:40Z","timestamp":1760108680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/13\/1352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":28,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["diagnostics14131352"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14131352","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,26]]}}}