Turkish Neurosurgery
Comparative analysis between machine learning algorithms and conventional regression in predicting the prognosis of patients with basilar invagination: a retrospective cohort study
Liwei Peng1, Chi Peng2, Fan Yang3, Wei Zuo1, Chao Cheng1, Peng Wang1, Jin’an Zhang1, Weixin Li1
1Tangdu Hospital, Fourth Military Medical University, Department of Neurosurgery, Xi\'an,
2Naval Medical University, Department of Health Statistics, Shanghai,
3Tangdu Hospital, Fourth Military Medical University, Department of Plastic Surgery and Burns, Xi\'an,
DOI: 10.5137/1019-5149.JTN.36068-21.3

Aim:We sought to identify predictors of basilar invagination (BI) prognosis and compare diagnostic properties between logistic modeling and machine learning methods.Material and Methods:We conducted a single-center retrospective study. Patients at our hospital who met the inclusion and exclusion criteria were identified between August 2015 and August 2020 for inclusion. Candidate predictors, such as demographics, clinical scores, radiographic parameters, and outcome, were included. The primary outcome was the prognosis evaluated by the change in patient-reported Japanese orthopaedic association (PRO-JOA) score. Conventional logistic regression models and machine learning algorithms were implemented. Models were compared, considering the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and calibration curve.Results:Overall, the machine learning algorithms and traditional logistic regression models performed similarly. The postoperative cervicomedullary angle, head–neck flexion angle (HNFA), atlantodental interval, postoperative clivo-axial angle, age, postoperative clivus slope, postoperative cranial incidence, weight, postoperative HNFA, and postoperative Boogaard’s angle (BoA) were identified as important predictors for BI prognosis. Among the surveyed radiographic parameters, postoperative BoA was the most important predictor of BI prognosis. In the validation dataset, the bagged trees model performed best (AUC, 0.90).Conclusion:Through machine learning, we have demonstrated predictors of BI prognosis. Machine learning methods did not provide too many advantages over logistic regression in predicting BI prognosis but remain promising.

Corresponding author : Weixin Li