ArticleJournal of personalized medicine2021
Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and Tree-Based AutoML in Breast Cancer Prediction.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- Effects of Ramadan fasting on physical capacity and plasma markers of neuromuscular junction degradation in older adult men.European journal of nutrition · 2026Article
- Plasma CAF22 and NfL reflect neuromuscular decline during cisplatin-based chemotherapy and radical cystectomy in muscle-invasive bladder cancer.International journal of clinical oncology · 2025Article
- Natural Language Processing andJMIR medical informatics · 2025Article
- Impact of Normal Lung Volume Choices on Radiation Pneumonitis Risk Prediction in Locally Non-small Cell Lung Cancer Radiation Therapy.Advances in radiation oncology · 2025Article
- New hybrid features extracted from US images for breast cancer classification.Scientific reports · 2025Article
- The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning.Patterns (New York, N.Y.) · 2025Article
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- ExAutoGP: Enhancing Genomic Prediction Stability and Interpretability with Automated Machine Learning and SHAP.Animals : an open access journal from MDPI · 2025Article
- Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.BMC cancer · 2025Article
- A Cost-Effective Two-Step Approach for Multi-Cancer Early Detection in High-Risk Populations.Cancer research communications · 2025Article
- Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.PloS one · 2025Article
- Exploring Voice Acoustic Features Associated with Cognitive Status in Korean Speakers: A Preliminary Machine Learning Study.Diagnostics (Basel, Switzerland) · 2024Article
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- Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.Journal of assisted reproduction and genetics · 2024Article
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- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC bioinformatics · 2024Article
- Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses.Journal of cancer research and clinical oncology · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network-multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method's performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis.
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