Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review

Ghavidel, Arman; Pazos, Pilar

doi:10.1007/s11764-023-01465-3

Cited by 12 publications

(5 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, in breast cancer, machine learning models have been used to predict the likelihood of recurrence and to guide the selection of adjuvant therapy. These models take into account various factors such as tumor size, grade, hormone receptor status, and genomic markers to make their predictions [ 25 , 26 , 27 , 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Daye,

Parker,

Tripathi

et al. 2024

Cancers

View full text Add to dashboard Cite

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm’s performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model’s predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.

show abstract

Section: Discussionmentioning

confidence: 99%

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Daye,

Parker,

Tripathi

et al. 2024

Cancers

View full text Add to dashboard Cite

show abstract

“…In the domain of machine learning, selecting features is a crucial procedure that boosts the efficacy of models by pinpointing and employing the key features from the data collection [26]. The utilized feature selection method in each of the selected models is shown in TABLE 3.…”

Section: Feature Selectionmentioning

confidence: 99%

“…An imbalanced data problem is a common challenge in predictive models based on clinical datasets because positive cases of a disease or diagnosis are typically part of a minority-class sample [26]. In this case, the majority class samples representing patients with a negative diagnosis are remarkably higher than the minority class samples.…”

Section: Approach To Imbalanced Data Problemmentioning

confidence: 99%

Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models

Ghavidel,

Pazos,

Del Aguila Suarez

et al. 2024

j.electron.electromedical.eng.med.inform

View full text Add to dashboard Cite

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques like grid search. A novel aspect of this research was the comparison of a newly developed DNN model with existing ensemble models based on similar cardiovascular datasets. The results indicated the DNN model's superior predictive accuracy, demonstrating an Area Under the Curve (AUC) of 74%, alongside notable precision (68%) and recall (72%) for the minority class, which indicates patients requiring surgery. The model further achieved a 70% F1-Score and a balanced accuracy rate of 72%, significantly outperforming the existing ensemble models in every key performance metric. The study underscores the transformative potential of DNNs in predictive modeling for cardiovascular care and highlights the importance of integrating advanced ML techniques into clinical workflows. Future research should delve into the practical application and integration of these models.

show abstract

“…Even with large sets of records, the number of ASD cases may be small, resulting in few training examples. Several techniques can be applied, such as collecting more data, over- and under-sampling of existing data, 31 creating synthetic data, eg, with large language models (LLM), 32 or applying techniques such as the Synthetic Minority Over Sampling Technique (SMOTE). 33 …”

Section: Introductionmentioning

confidence: 99%

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes

Leroy,

Andrews,

KeAlohi-Preece

et al. 2024

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. Methods We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules. One rule-based and three deep ML algorithms and six ensembles were compared and evaluated using a test set with 6773 sentences (N = 35 cases) set aside in advance. Criterion and case labeling were evaluated for each ML algorithm and ensemble. Case labeling outcomes were compared also with seven traditional tests. Results Performance for criterion labeling was highest for the hybrid BiLSTM ML model. The best case labeling was achieved by an ensemble of two BiLSTM ML models using a majority vote. It achieved 100% precision (or PPV), 83% recall (or sensitivity), 100% specificity, 91% accuracy, and 0.91 F-measure. A comparison with existing diagnostic tests shows that our best ensemble was more accurate overall. Conclusions Transparent ML is achievable even with small datasets. By focusing on intermediate steps, deep ML can provide transparent decisions. By leveraging data redundancies, ML errors at the intermediate level have a low impact on final outcomes.

show abstract

Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review

Cited by 12 publications

References 80 publications

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes

Contact Info

Product

Resources

About