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Diabetes heightens patients' risk of severe complications, including amputations and death, while also driving up healthcare system costs. Given the significance of this problem, the present study conducted a systematic review of studies that used Classical Statistical and Machine Learning Approaches to identify factors influencing the development of diabetic foot ulcers in individuals with diabetes. A thorough literature search was conducted in PubMed, Scopus and Web of Science, covering their inception through 7 September 2025. This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta\u2010Analyses (PRISMAs) guidelines. Data were analysed narratively using content analysis. Eligible studies used predictive methods to identify factors associated with the development of diabetic foot ulcers. A total of 4396 articles were screened, and 66 studies were selected for full\u2010text review after application of the inclusion and exclusion criteria. The review of these studies identified 95 factors associated with predicting diabetic foot ulcers, among which neuropathy, diabetes duration, age, body mass index and peripheral vascular disease were the most frequently reported. In addition, among the predictive models used in the studies, logistic and Cox regression models were the most useful for predicting factors associated with diabetic foot ulcers. This study identifies key predictive factors for diabetic foot ulcers, enabling healthcare systems to target high\u2010risk patients through early screening. Proactive identification of vulnerable diabetic patients can prevent severe complications, such as amputation.<\/jats:p>","DOI":"10.1111\/iwj.70982","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:19:07Z","timestamp":1783988347000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Factors Influencing the Incidence of Diabetic Foot Ulcers Using Classical Statistical and Machine Learning Approaches: A Systematic Review"],"prefix":"10.1111","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1566-5203","authenticated-orcid":false,"given":"Alireza","family":"Jafarkhani","sequence":"first","affiliation":[{"name":"Nursing Care Research Center, Clinical Sciences Institute Baqiyatallah University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7788-7063","authenticated-orcid":false,"given":"Mehdi Jafari","family":"Oori","sequence":"additional","affiliation":[{"name":"Nursing Care Research Center, Clinical Sciences Institute Baqiyatallah University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3955-613X","authenticated-orcid":false,"given":"Masoud","family":"Arabfard","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Health Research Center, Biomedicine Technologies Institute Baqiyatallah University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiavash","family":"Hushmandi","sequence":"additional","affiliation":[{"name":"Nephrology and Urology Research Centre, Clinical Sciences Institute Baqiyatallah University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Pourebrahimi","sequence":"additional","affiliation":[{"name":"Nursing Care Research Center, Clinical Sciences Institute Baqiyatallah University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.4239\/wjd.v6.i6.850"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2019.107843"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/bios14120614"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104838"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13047-020-00383-2"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.12968\/jowc.2021.30.Sup6.S34"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1159\/000439409"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtv.2024.07.004"},{"issue":"86","key":"e_1_2_10_10_1","first-page":"334","article-title":"Artificial Intelligence in Medicine","volume":"86","author":"Holmes J.","year":"2004","journal-title":"Annals of the Royal College of Surgeons of England"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-023-02376-0"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(17)30058-2"},{"issue":"1","key":"e_1_2_10_13_1","first-page":"3","article-title":"Supervised Machine Learning: A Review of Classification Techniques","volume":"160","author":"Kotsiantis S. 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