ArticleHealthcare (Basel, Switzerland)2023
Application of SHAP for Explainable Machine Learning on Age-Based Subgrouping Mammography Questionnaire Data for Positive Mammography Prediction and Risk Factor Identification.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review.BMJ health & care informatics · 2024Pooled it
- Explainable artificial intelligence in ICU admission prediction for methanol poisoning patients: a comparative study of deep and machine learning models.International journal of emergency medicine · 2026Article
- UbiQTree: Uncertainty quantification in XAI with tree ensembles.Patterns (New York, N.Y.) · 2026Article
- Metabolic dynamic score and machine learning: a novel approach to predicting pathological complete response in rectal cancer after neoadjuvant chemoradiotherapy.Frontiers in oncology · 2026Article
- Enhancing happiness and life satisfaction in university students: analysis with a machine learning approach.BMC psychology · 2025Article
- Personalized colorectal cancer risk assessment through explainable AI and Gut microbiome profiling.Gut microbes · 2025Article
- Optimization of ultrasonic extraction and kinetic modeling of jieduquyuziyin prescription polysaccharides via explainable machine learning.Ultrasonics sonochemistry · 2025Article
- Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.International journal of colorectal disease · 2025Article
- Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges.Healthcare (Basel, Switzerland) · 2025Review
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- Predicting Major Preoperative Risk Factors for Retears After Arthroscopic Rotator Cuff Repair Using Machine Learning Algorithms.Journal of clinical medicine · 2025Article
- Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire.Scientific reports · 2025Article
- Interpretable machine learning model to predict 90-day radiographically confirmed pneumonia after chemotherapy initiation in non-Hodgkin lymphoma: development and internal validation of a single-center cohort.Frontiers in medicine · 2025Article
- Interpretable machine learning models based on multi-dimensional fusion data for predicting positive surgical margins in robot-assisted radical prostatectomy: a retrospective study.Frontiers in oncology · 2025Article
- Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.Scientific reports · 2024Article
- The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children.Medicina (Kaunas, Lithuania) · 2024Article
- Article
- Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.Computational and structural biotechnology journal · 2024Article
- Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning.Cancers · 2024Article
- Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Mammography is considered the gold standard for breast cancer screening. Multiple risk factors that affect breast cancer development have been identified; however, there is an ongoing debate regarding the significance of these factors. Machine learning (ML) models and Shapley Additive Explanation (SHAP) methodology can rank risk factors and provide explanatory model results. This study used ML algorithms with SHAP to analyze the risk factors between two different age groups and evaluate the impact of each factor in predicting positive mammography. The ML model was built using data from the risk factor questionnaires of women participating in a breast cancer screening program from 2017 to 2021. Three ML models, least absolute shrinkage and selection operator (lasso) logistic regression, extreme gradient boosting (XGBoost), and random forest (RF), were applied. RF generated the best performance. The SHAP values were then applied to the RF model for further analysis. The model identified age at menarche, education level, parity, breast self-examination, and BMI as the top five significant risk factors affecting mammography outcomes. The differences between age groups ranked by reproductive lifespan and BMI were higher in the younger and older age groups, respectively. The use of SHAP frameworks allows us to understand the relationships between risk factors and generate individualized risk factor rankings. This study provides avenues for further research and individualized medicine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.