ArticleBMC medical research methodology2022
Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning-based risk predictive model for postoperative refractures in patients with osteoporotic vertebral compression fractures: a systematic review and critical appraisal.BMC musculoskeletal disorders · 2026Pooled it
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- Prediction models for treatment response in migraine: a systematic review and meta-analysis.The journal of headache and pain · 2025Pooled it
- Stereotactic Radiation Therapy for Lung Cancer Brain Metastases: Dose Optimization and Prognostic Prediction via Biologically Effective Dose-Based Empirical Dose-Response Modeling and Extreme Gradient Boosting-SHapley Additive Explanations Machine Learning.Advances in radiation oncology · 2026Article
- Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.BMC cancer · 2026Article
- Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis.Lipids in health and disease · 2026Observational
- [A prediction model for early neurological improvement after intravenous thrombolysis in patients with acute ischemic stroke].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Optimized KNN with domain-informed features and LIME explainability for improved breast cancer classification.BMC medical informatics and decision making · 2026Article
- Development and validation of a cardiometabolic multimorbidity prediction model in middle-aged and older adults.Scientific reports · 2026Article
- Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.Biological trace element research · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- A machine learning-based online prediction model for recurrence risk in patients withFrontiers in cellular and infection microbiology · 2026Article
- An interpretable XGBoost model for contrast-associated acute kidney injury in patients with acute coronary syndrome: development and geographical external validation.Frontiers in digital health · 2026Article
- Article
- Development and explainability of a machine learning prediction model for histological prostatic inflammation in surgically treated patients with benign prostatic hyperplasia: a single-center internal validation study.Frontiers in medicine · 2026Article
- Predicting non-compliance to pancreatic enzyme supplementation therapy in chronic pancreatitis: A machine learning-based approach.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2025Article
- Reforming disease prognosis and treatment prediction for palliative care with hybrid metaheuristic deep neural architectures in IoT healthcare ecosystems.Scientific reports · 2025Article
- Deciphering N7-methylguanosine-driven immune dysregulation in unexplained recurrent spontaneous abortion based on transcriptome data and experimental validation.European journal of medical research · 2025Article
- Predicting proximal junctional failure in adult spinal deformity patients using machine learning models based on spinal alignment parameters.Scientific reports · 2025Article
- Artificial intelligence in antiphospholipid syndrome: toward individualized risk prediction.Research and practice in thrombosis and haemostasis · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical example of techniques that facilitate the development of high-quality ML systems including data pre-processing, hyperparameter tuning, and model comparison using open-source software and data.
methodsWe used open-source software and a publicly available dataset to train and validate multiple ML models to classify breast masses into benign or malignant using mammography image features and patient age. We compared algorithm predictions to the ground truth of histopathologic evaluation. We provide step-by-step instructions with accompanying code lines.
findingsPerformance of the five algorithms at classifying breast masses as benign or malignant based on mammography image features and patient age was statistically equivalent (P > 0.05). Area under the receiver operating characteristics curve (AUROC) for the logistic regression with elastic net penalty was 0.89 (95% CI 0.85 - 0.94), for the Extreme Gradient Boosting Tree 0.88 (95% CI 0.83 - 0.93), for the Multivariate Adaptive Regression Spline algorithm 0.88 (95% CI 0.83 - 0.93), for the Support Vector Machine 0.89 (95% CI 0.84 - 0.93), and for the neural network 0.89 (95% CI 0.84 - 0.93).
interpretationOur paper allows clinicians and medical researchers who are interested in using ML algorithms to understand and recreate the elements of a comprehensive ML analysis. Following our instructions may help to improve model generalizability and reproducibility in medical ML studies.
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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.