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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77721, first published .
Doctor's gloved hands hold wooden blocks spelling "data

Beyond Comparing Machine Learning and Logistic Regression in Clinical Prediction Modelling: Shifting from Model Debate to Data Quality

Beyond Comparing Machine Learning and Logistic Regression in Clinical Prediction Modelling: Shifting from Model Debate to Data Quality

Journals

  1. Wang K, Wu P, Chen Y, Wang H. An AI-Based Radiomics Model Using MRI ADC Maps for Accurate Prediction of Advanced Prostate Cancer Progression. Current Oncology 2026;33(1):35 View
  2. Cruz-Fernandez M, Castillo-Velásquez F, Fuentes-Silva C, Rodríguez-Abreo O, Rojas-Galván R, Avilés M, Rodríguez-Reséndiz J. Predicting Breast Cancer Mortality Using SEER Data: A Comparative Analysis of L1-Logistic Regression and Neural Networks. Technologies 2026;14(1):66 View
  3. Wang Y, Wang J, Chen H. Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application. Journal of Translational Medicine 2026;24(1) View
  4. Ko H, Martin B, Pelt C, Pellegrini V. Comparative analysis of supervised machine learning models for predicting surgeon fixation choice in total knee arthroplasty. BMC Medical Informatics and Decision Making 2026;26(1) View
  5. Condoiu C, Baloi A, Sandesc D, Cumpanas A, Latcu S, Dema V, Caprariu R, Barb A, Ciucurita A, Marinescu A, Cut T, Bardan R. Clinically Significant ISUP Upgrading in the Multiparametric MRI Era: Biopsy Tumor Burden Outperforms Complex Machine Learning Models in a Single-Center Exploratory Cohort. Cancers 2026;18(5):730 View
  6. Benítez-Andrades J, Laballos-González L, Pujante-Otalora L, Nieto-Marcos S, Pinto-Carral A, Álvarez-Álvarez M. Machine learning-based prediction of disability in subacute low back pain: a primary care study on clinical and psychosocial determinants. BMC Medical Informatics and Decision Making 2026;26(1) View
  7. Quach J, Theou O, Maxwell S, Abidi S, Song X, Rockwood K, Kehler D. Machine learning for frailty assessment and outcome prediction in cardiovascular disease: A scoping review. Ageing Research Reviews 2026;118:103122 View
  8. Wang H, Wang H, Li N, Qie S, Dong J, Wang H. Development of machine learning models for preoperative prediction of insufficient clinical improvement after anterior cruciate ligament reconstruction. BMC Musculoskeletal Disorders 2026;27(1) View
  9. Maulana M, Saputro S, Faisal M, Nugroho R, Ramadhan A. Enhancing Software Defect Prediction through Hybrid Multi-Filter Feature Selection and Imbalance Handling. Journal of Computing Theories and Applications 2026;3(4):518 View
  10. Talavera-Cobo V, Robles-Garcia J, Guillen-Grima F, Calva-Lopez A, Tapia-Tapia M, Labairu-Huerta L, Ancizu-Marckert F, Guillen-Aguinaga L, Sanchez-Zalabardo D, Miñana-Lopez B. Prioritising Data Quality Governance for AI in Prostate Cancer: A Methodological Proof-of-Concept Study Using Neural Networks for Risk Stratification. Diagnostics 2026;16(10):1454 View
  11. Gao Y, Xu D, Li X, Wang J, Wang L, Wu B, Zhao H, Qiu X, Zhu W. Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence. Journal of Medical Internet Research 2026;28:e84844 View
  12. Liu W, Xie Q, Liu C, Chen B, Yang X, Yang L, Yu H. A multicenter, clinically interpretable prediction model for malignancy risk in C-TIRADS 3–4 thyroid nodules. Frontiers in Oncology 2026;16 View
  13. Ndubuisi J, Chimaoge Henry I, Ibekie O, Oniah W, Purity G, Nnamani J, Omofuma O, Chimdike U, Divine N, Obidiegwu I, Jessica Ifunanya U, Okonkwo P, Ibekie O, Chinenye Benita N, Ashinze P. Artificial Intelligence for Neonatal Sepsis Prediction: A Review of Advances, Clinical Integration, and Future Prospects. Cureus 2026 View
  14. Hidayat R, Fauzia F, Kusumo Wibowo S, Ariane A, Partan R, Muthia P, Warlisti I, Suntoko B, Pratama M, Wahono C, Rahmawati L, Kurniari P, Utomo A, Najirman N, Kurniawan E, Werdiningsih Y, Pango F, Dhifanra M, Audrey J, Parlindungan F. Development and validation of a clinical scoring system for the prediction of methotrexate treatment response in rheumatoid arthritis patients: a multicenter real-world study. Therapeutic Advances in Musculoskeletal Disease 2026;18 View
  15. Kume K, Taira T, Terao S, Sakai N. In vitro development of the Autonomous Colonoscope Robot System (ACRS) for fully automated colonoscope insertion. Communications Medicine 2026;6(1) View
  16. Hefny N, Mourgan F, Atiyah D, AlMatar M. Machine Learning-based Classification of Exercise Status Using miR-21 Expression and Multivariable Participant Profiles. Biomedical and Biotechnology Research Journal 2026;10(2):238 View
  17. Li C, Wang X, Zhang C, Rao J, Jiang B, Xiang Y, Liu Y, Tang F. Early identification of Kawasaki disease using an interpretable machine learning model with robust external validation and clinical utility assessment. European Journal of Medical Research 2026;31(1) View
  18. Shuo Z, Hui D, Long Z, Qiang D, Chuan H, Xu H, Apaer S, Ling C. Development and internal validation of a machine learning–based model for predicting postoperative complications after primary liver cancer resection. BMC Surgery 2026;26(1) View
  19. Namaganda M, Gennatas S, Merson L, Garcia E, Edinburgh T, Nabende J, Kateete D, Batte C, Wayengera M, Jjingo D, Kigozi E, Mboowa G. Predicting HIV viral non-suppression in Uganda: development and validation of machine learning and risk stratification models using routine EMR data. Frontiers in Artificial Intelligence 2026;9 View
  20. Liu Y, Zhou Y, Zhang Y, Tan J, Gong J. Multimodal machine learning predicts type 2 respiratory failure in COPD exacerbations: a multicenter XGBoost model with clinical nomogram. Frontiers in Medicine 2026;13 View
  21. Jin C, Dai Q, Ren X, Shen J. Multi-dimensional risk prediction and genetic architecture of aspiration pneumonia: a population-based analysis. BMC Infectious Diseases 2026;26(1) View
  22. Catic A, Karamitros G, Lineaweaver W, Lamaris G, Pratibh P, Manson P, Redett R, Barbieri S, Jorm L. The FACT-Score: Development and Temporal Validation of a Clinical Risk Stratification Tool for 30-Day Complications After Operative Facial Fracture Repair. Journal of Craniofacial Surgery 2026 View
  23. Borkovski A, Kuczera M, Borkovski A, Silveira A, Basso I, Baratto-Filho F, Küchler E, Guariza-Filho O, de Araujo B, Schroder A, de Araujo C. Use of the mental foramen as a marker for sex estimation using supervised machine learning. Egyptian Journal of Forensic Sciences 2026;16(1) View
  24. Fonseca T, Von Rekowski C, Araújo R, Justino G, Oliveira M, Bento L, Calado C. Metabolomic Signatures of Biotrauma Associated with Mortality in ICU Patients Requiring Invasive Mechanical Ventilation and ECMO. Metabolites 2026;16(7):516 View
  25. Lv J, Wang X, Zhang Z. An interpretable machine learning model for predicting 1-year major adverse cardiovascular events in patients with type 2 diabetes and hypertension. Frontiers in Medicine 2026;13 View
  26. Andrés-Sánchez J, Belzunegui-Eraso Á, Pastor-Gosálbez I, Sánchez-Aragón A. Predicting Pornography Use Among Adolescents in Spain: Findings from Ensemble Tree Models and Explainable Machine Learning. Computation 2026;14(8):177 View
  27. Zhang S, Kang L, Yang S, Su X, Lin R, Shariff N. Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis. BMC Psychiatry 2026;26(1) View
  28. Chiu H, Chuang Y, Zaami S, Chen T. Algorithmic Prognostication in Female Oncofertility Counseling: Ethical Challenges of Bias, Autonomy, and Predictive Uncertainty. Healthcare 2026;14(16):2538 View
  29. Meng X, Zhang S, Lin Y, Fang Y, Huang P, Wen J, Yang X, Ji G, Peng J, Wu W. An explainable machine learning method for identifying key atherogenic lipid biomarkers in abdominal obesity among the Southern Chinese population. Lipids in Health and Disease 2026;25(1) View
  30. Md. Nasim Ali , Ayub M, Rana S. Performance Evaluation of Supervised Machine Learning Models for Heart Disease Prediction Using Clinical Survey Data. Revista de Informática Teórica e Aplicada 2026;33(4):11 View
  31. Su Y, Wang D, Zhao Y, Xing X. Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new stage. World Journal of Gastroenterology 2026;32(34) View
  32. Li P, Yan C, Li J, Zheng Q, Zhang H. Machine learning-based identification of anxiety symptoms in Chinese community-dwelling older adults: a comparative study of six algorithms with SHAP analysis and nomogram development. Frontiers in Psychiatry 2026;17 View
  33. Wang X, Fu Y, Chen J, Wan J, Kong W. Development and internal validation of an interpretable machine learning model for predicting vancomycin-induced nephrotoxicity in hospitalized children. International Journal of Clinical Pharmacy 2026;48(6):2262 View
  34. Kim S, Bae S. Development of a predictive model for high-flow nasal cannula treatment failure in emergency department patients with acute heart failure: a retrospective secondary data analysis. Journal of Korean Biological Nursing Science 2026;28(3):528 View
  35. Zhang J, You T, Li J, Dong J, Xu L, Li X, Hu J, Tang Y, Hou M, Liu Y, Xu Z, Lv H, Huang H. Machine learning model for intravenous immunoglobulin resistance in Kawasaki disease: model development and validation study. World Journal of Pediatrics 2026;22(8):939 View
  36. Ray E, Zhang X, Dunham L, Tan X, Elston Lafata J, Reeder-Hayes K. Development and Validation of a Metastatic Breast Cancer–Specific Prognostic Model Using CancerLinQ Discovery. JCO Oncology Practice 2026 View
  37. Li H, Feng X, Zheng X, Zhong X. Systematic review and meta-analysis of risk prediction models for anti-tuberculosis drug-induced liver injury in East Asian populations. Frontiers in Public Health 2026;14 View
  38. Sun R, Liu Y, Lu S, Tartari E, Birgand G, Yang L, Zhou L. Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review. Journal of Medical Internet Research 2026;28:e94617 View
  39. Yang L, Ning B, Hu D, Xiong H, Ge T, Fan X, Zou R. Machine learning algorithms for predicting perioperative myocardial injury in elderly patients: a comparative review. Current Opinion in Physiology 2026;50:100987 View
  40. Sivaramakrishnan G, Alselait M, Basri J, AlThawadi B, Ansari S, Alalawi S, Sridharan K, AlMuharraqi M. Limits of questionnaire-based prediction of oral health-related quality of life in non-smokers. BDJ Open 2026;12(1) View
  41. Elkrief L, Gonzalez B, Boubekri S, Do A, Trottier-Duclos F, Miron J, Garel N, Briganti G, Lespérance P, Wade B. Predicting remission following repetitive transcranial magnetic stimulation in treatment-resistant depression: a comparison of parsimonious and ensemble machine learning models. Journal of Affective Disorders 2027;416:122602 View
  42. Hsu J, Cheng H, Chen Y, Hsieh H, Li A, Lin J. Navigating machine learning and classical statistics in sarcopenia screening for community-dwelling older women. Journal of the Formosan Medical Association 2026 View
  43. Wang P, Zhao J, Huang J, Tang Y, Liu J, Wei X, Zhang M. Development and validation of a machine learning-based prediction model for emergence agitation in geriatric patients undergoing orthopedic surgery: a prospective cohort study. Frontiers in Medicine 2026;13 View
  44. Escandón-Villalobos E, Elguezabal-Rodelo R, Pardiño Vega M, Medina-Montalvo L, Godinez-Castillo G, Montalvo-Jave E, Faes Petersen R, Villegas-Tovar E. Artificial Intelligence Versus Logistic Regression for Postoperative Mortality Prediction: A Systematic Review and Meta-Analysis. Cureus 2026 View
  45. Flores E, Meza D, Gonzales R, Agudelo-Santos C, Sosa L, Espinoza L, Zablah I. Explainable Classification Models for Identifying Elevated Gastrointestinal Symptom Burden in Honduran Communities: A Cross-Sectional Study. Diagnostics 2026;16(21):3440 View
  46. Bayan K, Kholmurod Q, Yadav R. Comment on “Polygenic risk score for early identification of coronary artery disease in a real-world clinical setting within the Latvian patient population”. American Heart Journal Plus: Cardiology Research and Practice 2026;72:100984 View
  47. Yu Y, Jing Y, Tang Y, Liu F, Wei H. External validation of a machine learning model for predicting liver injury in children with mycoplasma pneumoniae pneumonia. Scientific Reports 2026;16(1) View
  48. Yang X, Cao Y, Wang H, Li H, Jiang X, Xu H, Yao T, Zhang X, Chen Y, Li J, Zhou B, Xu L, Chen Z, Zhu X. SDS screen-positive prediction from structured questionnaires in university students: a comparative evaluation of large language model prompting strategies. Frontiers in Psychiatry 2026;17 View
  49. Keß S, Nordblom N, Geim L, Stellzig-Eisenhauer A, Kunz F. Reply to: Letter to editor regarding “Influence of general orthodontic treatment need and of specific anterior malocclusions on oral health-related quality of life in children and adolescents with class II malocclusion”. Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie 2026 View
  50. Fordham K, Monk A, Bjarnadottir R, Cousin L, Zhong X, Yao Y, Parker L. Predictors and Risk Factors of Necrotizing Enterocolitis. Advances in Neonatal Care 2026 View
  51. Yang S, Li J, Chen L, Min J, Li L, Liu G. Development and external validation of a machine learning model for predicting stroke-associated pneumonia in acute ischemic stroke patients receiving pantoprazole. Scientific Reports 2026;16(1) View
  52. Grzybowska U, Wojewódzka-Wiewiórska A, Dudek H. Household Factors Associated with Holiday Deprivation in Poland: Evidence from Tree-based Gradient Boosting Methods and Logistic Regression. Procedia Computer Science 2026;292:3031 View
  53. TANS M, UIJL A, ASSELBERGS F, SAVARESE G. Glass Box, Familiar View: Interpretable Machine Learning for the Prediction of Incident Heart Failure. Journal of Cardiac Failure 2026;32(11):1954 View

Conference Proceedings

  1. Kakharov A, Kurmanbek S, Tasmurzayev N, Kaibullayeva J, Boltaboyeva A, Amirkhanova G. 2026 IEEE 6th International Conference on Smart Information Systems and Technologies (SIST). Causal AI Framework for Treatment Effect Estimation in Cardiovascular Disease Risk Analysis View
  2. Sommerfield D, Obraczka K, Casper J, Chudasama H, Engstrom C. 2026 IEEE International Conference on Digital Health (ICDH). SAPIF: A Severity-Aware Framework for Pressure Injury Forecasting View