Results 1 to 10 of about 25,341,143 (317)

A problem-agnostic approach to feature selection and analysis using SHAP

Journal of Big Data
Feature selection is an effective data reduction technique. SHapley Additive exPlanations (SHAP) can be used to provide a feature importance ranking for models built with labeled or unlabeled data. Thus, one may use the SHAP feature importance ranking in
John T. Hancock, Taghi M. Khoshgoftaar, Qianxin Liang   +2 more
doaj   +5 more sources

Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births [PDF]

BMC Pregnancy Childbirth
This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settings.We conducted a retrospective multicenter cohort study in Northeast ...
Song Z, Lin H, Shao M, Wang X, Chen X, Zhou Y, Zhang D.   +6 more
europepmc   +5 more sources

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis [PDF]

European Journal of Radiology
For patients suffering from differentiated thyroid cancer (DTC), several clinical, laboratory, and pathological features (including patient age, tumor size, extrathyroidal extension, or serum thyroglobulin levels) are currently used to identify recurrence risk. Validation and potential adjustment of their individual and combined prognostic values using
Andreas Schindele, Anne Krebold, Ursula Heiß, Kerstin Nimptsch, Elisabeth Pfaehler, Christina Berr, Ralph A. Bundschuh, Thomas Wendler, Olivia Kertels, Johannes Tran-Gia, Christian H. Pfob, Constantin Lapa   +11 more
openaire   +3 more sources

Enhanced and Interpretable Prediction of Multiple Cancer Types Using a Stacking Ensemble Approach with SHAP Analysis. [PDF]

Bioengineering (Basel)
Background: Cancer is a leading cause of death worldwide, and its early detection is crucial for improving patient outcomes. This study aimed to develop and evaluate ensemble learning models, specifically stacking, for the accurate prediction of lung ...
Ganie SM, Dutta Pramanik PK, Zhao Z.
europepmc   +2 more sources

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development. [PDF]

Clin Transl Sci
Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions.
Ponce-Bobadilla AV, Schmitt V, Maier CS, Mensing S, Stodtmann S.   +4 more
europepmc   +2 more sources

Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis. [PDF]

Sci Rep
The increasing demand for sustainable construction materials has led to the incorporation of Palm Oil Fuel Ash (POFA) into concrete to reduce cement consumption and lower CO₂ emissions.
Ali T, Onyelowe KC, Mahmood MS, Qureshi MZ, Kahla NB, Rezzoug A, Deifalla A.   +6 more
europepmc   +2 more sources

Integrating Machine Learning and SHAP Analysis to Advance the Rational Design of Benzothiadiazole Derivatives with Tailored Photophysical Properties. [PDF]

J Chem Inf Model
2,1,3-Benzothiadiazole (BTD) derivatives show promise in advanced photophysical applications, but designing molecules with optimal desired properties remains challenging due to complex structure–property relationships. Existing computational methods have
Veríssimo RF, Matias PHF, Barbosa MR, Neto FOS, Neto BAD, de Oliveira HCB.   +5 more
europepmc   +2 more sources

Predicting honest behavior based on Eysenck personality traits and gender: an explainable machine learning study using SHAP analysis. [PDF]

Front Psychol
Introduction This study bridges a critical gap in aviation safety research by examining how Eysenck personality traits (Neuroticism, Psychoticism, Extraversion) and gender predict dishonest behavior in high-risk aviation contexts.
Meng Y, Peng Z, Zhang Z, Chen Q, Huang H, Chen Y, Zhao M.   +6 more
europepmc   +2 more sources

Predicting postoperative malnutrition in patients with oral cancer: development of an XGBoost model with SHAP analysis and web-based application. [PDF]

Front Oncol
Background Postoperative malnutrition, which significantly affects recovery and overall quality of life, is a critical concern for patients with oral cancer.
Kuang L, Yu J, Zhou Y, Zhang Y, Wang G, Zhang F, Lubamba GP, Bi X.   +7 more
europepmc   +2 more sources

Machine Learning Interpretability in Diabetes Risk Assessment: A SHAP Analysis

Computers and Electronics in Medicine
Diabetes continues to be a complicated and prevalent metabolic illness, providing a serious burden to public health. While machine learning approaches like extreme gradient boosting (XGBoost) provide intriguing options for diabetes prediction, their 'black-box' nature typically limits clinical interpretability.
Mustafa Kutlu, Turker Berk Donmez, Chris Freeman   +2 more
openaire   +3 more sources