Jul 2026· World Journal of Gastrointestinal Surgery· Vol 18· 0 citations· 56 references
TL;DR
It is concluded that future research must construct a more rigorous evidence chain within multicenter and multimodal frameworks, specifically regarding missing data mechanisms, sensitivity analyses, calibration and net benefit assessments, and the availability of reproducible materials to substantiate generalizable clinical utility.
Abstract
The convergence of artificial intelligence and precision oncology is frequently hampered by the quality of real-world clinical data, particularly the pervasive challenge of missing values. This opinion review critically appraises the methodology and evidentiary framework of the study, which proposes a hybrid imputation architecture, HDI-MF-Gower, integrated with an extra trees classifier and Shaply Additive exPlanation interpretability for predicting survival outcomes following curative gastrectomy. We deconstruct the pivotal assumptions and potential sensitivities of their adaptive weighted similarity initialization. This design is engineered to provide a “warm start” aligned with the underlying data structure for iterative imputation, theoretically mitigating the risks of distributional distortion associated with simplistic initialization strategies. However, a primary boundary of the current evidence lies in the validation hierarchy; the reported validation relies predominantly on random splitting within a single-center cohort, lacking the robustness of temporal extrapolation or genuine external validation. Furthermore, statistical comparisons suggest that the performance differences between the proposed model and several robust ensemble baselines are not consistently distinguishable, making it difficult to attribute performance gains solely to the specific choice of the learner. We conclude that future research must construct a more rigorous evidence chain within multicenter and multimodal frameworks. Crucially, adherence to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis + artificial intelligence guidelines - specifically regarding missing data mechanisms, sensitivity analyses, calibration and net benefit assessments, and the availability of reproducible materials - is essential to substantiate generalizable clinical utility.
A rigorous empirical framework is presented for comparing three uncertainty quantification approaches on two clinical prediction tasks, in-hospital mortality and 30-day readmission, using 74,829 ICU admissions from the MIMIC-IV database to support a more demanding evaluation standard for UQ in clinical machine learning.
Isaac Tosin Adisa, Francis Mawutor Amuyao, Ezekiel Olaoluwa Joaquim· International journal of re...· 0 citations
Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility. We introduce a statistically grounded framework that provides fully interpretable, rule-based clinical classification using the Bernoulli Na\"ive Bayes (BNB) model. The method applies supervised $\chi^2$-guided statistical binarization to continuous variables, identifying thresholds that maximize association with clinical outcomes within the training data. This transformation allows BNB to operate effectively on continuous medical data without sacrificing its inherent transparency. The approach was evaluated on three benchmark datasets, Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction, achieving area-under-the-curve (AUC) scores of 0.800 for the Pima analysis, 0.984 for Wisconsin Breast Cancer, and 0.919 for Heart Failure Prediction. In addition to discrimination, probabilistic reliability was assessed using leakage-safe cross-validated calibration analysis including Brier score, calibration intercept/slope, and post-hoc beta calibration, which improved probability calibration across datasets. These results suggest that a statistically interpretable framework can achieve performance comparable to more complex models while providing explicit, clinically meaningful decision rules and calibrated risk estimates. To illustrate this transparency concretely, a complete worked example demonstrates that model inference can be reproduced using only a reference table and basic arithmetic, without access to software or proprietary tools. This work offers a practical approach to supporting trustworthy and generalizable AI in real-world healthcare settings.
Antony García, A. Noriega, Gabrielle Britton et al.· arXiv.org· 0 citations
Multinomial Logistic Regression (MLR) remains one of the most widely used interpretable models for multiclass classification, risk prediction, and discrete decision analysis. Its continued relevance does not reflect the novelty of the classical model, but the new demands placed on it by high-dimensional, noisy, imbalanced, and privacy-constrained data. This PRISMA-guided systematic review analyzes 108 peer-reviewed journal articles published or first made available online between January 2015 and December 2024, supported by a structured quantitative coding of the entire verified corpus. The bibliometric analyses and the methodological counts are both based on all 108 included studies. Four review questions examine the evolution of the literature, methodological extensions for modern data regimes, validation and reporting practices, and the derivation of an integrated research framework. The coded evidence reveals a method–validation–deployment gap: regularization and feature selection are common (feature selection in 48% of studies and penalized MLR in 29%), whereas the structured record documents class-specific metrics in 9% of studies, probability-calibration reporting in 1%, and code-availability reporting in 11%. The extraction fields do not support a defensible corpus-wide external-validation rate, and IIA or category-dependence assessment is documented in only 2% of the corpus. To make these findings actionable, we distinguish MLR-specific failure modes (IIA, separation, reference-category dependence, and calibration) from generic machine-learning concerns and propose an integrated framework spanning model scope, modeling approach, enabling technologies, and implementation and deployment, linking data regimes to suitable MLR strategies, validation requirements, and reporting expectations.
Razan Alkhanbouli, Ping Ji, H. Jelinek et al.· IEEE Access· 0 citations
Diabetes prediction plays an important role in re-ducing long-term health risks by enabling early medical interven-tion.
Although machine learning models have been widely applied to this task, many existing studies emphasise predictive accuracy
while giving comparatively little attention to the reliability, interpretability, and stability of the resulting decisions. This paper
develops a reliability-aware and interpretable machine learning framework for diabetes prediction from structured clinical data.
Three complementary models—Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost)—are trained
on the Pima Indians Diabetes dataset so that both simple linear and complex non-linear relationships are captured. Beyond
conventional discrimination metrics, the reliability of the predicted probabilities is quantified using the Brier score and reliability
(calibration) diagrams. Interpretability is addressed with SHapley Additive exPlanations (SHAP) at both the global (cohort) and
local (individual patient) levels. Because different models frequently emphasise different predictors, we formalise a Feature
Consistency Index (FCI) that quantifies the cross-model agreement of SHAP-derived feature importance and combines it with
normalised importance into a single ranking score. Finally, a perturbation-based robustness analysis measures the sensitivity of
each model’s output to small changes in the input record. Experi-mentally, XGBoost achieves the highest discrimination
(accuracy 0.7597, ROC-AUC 0.8374), whereas Random Forest attains the best-calibrated probabilities (Brier score 0.1646),
demonstrating that discrimination and reliability are not interchangeable. The FCI identifies Glucose and BMI as
simultaneously the most influential and the most consistently attributed predictors, while Blood Pressure and Skin Thickness are
both weak and unstable. Under a 5% Gaussian perturbation of a representative patient record, the linear and bagged models
shift by less than 0.01 in predicted probability, whereas the boosted model shifts by 0.0386, revealing an accuracy–stability
trade-off that a purely accuracy-driven evaluation would not expose
R. V., S. Sasirekha· International Journal for Re...· 0 citations
Background: Diabetes mellitus is a major global health burden, and its early detection is essential for preventing serious complications. Machine learning, and eXtreme Gradient Boosting (XGBoost) in particular, performs strongly on routine clinical data; however, reported results are frequently optimistic because of hold-out evaluation and data leakage, and the resulting models are often difficult to interpret.
Objective: To develop and rigorously evaluate an interpretable XGBoost model for predicting the presence of diabetes from eight routine diagnostic measurements, using an unbiased, leakage-controlled evaluation design.
Methods: An openly available dataset of 1,168 patient records (771 non-diabetic and 397 diabetic; an approximately 66:34 class imbalance) with eight clinical features was analysed. Physiologically implausible zero values were treated as missing and imputed with the median inside a processing pipeline. Model selection and performance estimation were separated using a 5×5 nested cross-validation scheme, with all preprocessing confined to the training folds to prevent data leakage. Performance was assessed on out-of-fold predictions using accuracy, sensitivity, specificity, precision, F1 score, the area under the ROC curve (ROC-AUC), and the Brier score; probability calibration was examined; and the contribution of each feature was quantified with SHapley Additive exPlanations (SHAP).
Results: The model achieved an ROC-AUC of 0.823, an accuracy of 0.769, a specificity of 0.859, a sensitivity of 0.595, a precision of 0.684, an F1 score of 0.636, and a Brier score of 0.160. The near-diagonal calibration curve and low Brier score indicated that the predicted probabilities were well calibrated and could be interpreted as reliable risk estimates. SHAP analysis identified glucose, body mass index, and age as the most influential predictors, in agreement with established clinical risk factors for diabetes.
Conclusions: Under a leakage-controlled, unbiased evaluation, XGBoost provided moderate but trustworthy discrimination together with well-calibrated probabilities for diabetes prediction, while SHAP confirmed clinically plausible predictors. The comparatively conservative performance underscores the importance of nested cross-validation over simple hold-out splits for realistic model assessment. External validation and decision-threshold or class-imbalance strategies represent promising directions for future work.
Z. Kucukakcali, I. Cicek· International Journal of Med...· 0 citations
ICU mortality models can achieve strong discrimination, yet a risk score alone provides limited context for patient-level interpretation. We developed a multidimensional prediction-context framework that complements a calibrated mortality estimate with model behavior, data availability, recent physiology, and model attribution. Using 3,236 held-out ICU episodes from the MIMIC-III in-hospital mortality benchmark, we compared LSTM, GRU-D, XGBoost, and a weighted ensemble. The ensemble achieved an AUROC of 0.871 and AUPRC of 0.536; validation-based logistic recalibration improved the Brier score from 0.134 to 0.075. Incorrect predictions showed greater component-model disagreement and smaller decision-boundary margins, although model agreement and large margins did not guarantee correctness. Observation coverage and recent physiological trends also varied substantially across patients, highlighting differences in the information surrounding otherwise similar risk estimates. SHAP analysis attributed 89.9% of total absolute XGBoost attribution to physiological-value features and 10.1% to observation-process features. These dimensions were integrated into patient-level profiles to provide a more complete view of how predictions were formed and the clinical and data context surrounding them, extending interpretation beyond risk scores and feature rankings alone. Abbreviations: ICU (Intensive care unit)
Unknown authors· medRxiv· 0 citations
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