Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification

Ahuja, A. T.; Al-Zogbi, Lidia; Krieger, Axel

doi:10.1016/j.compbiomed.2021.104576

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…Finally, the MLP model is characterized as one of the best predictors, this predictor learns a feature from a set of inputs and combines the different features in a set of outputs, the performance rate of this model has been 99%, and it is a result with a high pre-accuracy rate, which allows it to be a reliable option for the prediction of breast cancer. Also, [20], [21] used this model with three clinical factors: age, cancer cell type, and cell surface receptors, obtaining satisfactory results, with a performance rate of 98%. The summary of the analysis of the 6 models used in this work to predict breast cancer is presented in Table V.…”

Section: J Model Training and Testingmentioning

confidence: 99%

“…Using features associated with cancer cell imaging, breast cancer can be predicted using ML models. This field of action is in constant development from two deans to after [19], [20].…”

Section: Introductionmentioning

confidence: 99%

“…This paper uses the Wisconsin breast cancer diagnostic dataset to predict and diagnose the likelihood of breast cancer in patients by analyzing six ML models. The dataset is composed of digitized mammogram images and consists of 569 observations and 31 attributes [21]. It also incorporates nine parameters set on a scale of 1 to 10 with values categorized into "benign" or "malignant" tumors.…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Prediction using Machine Learning Models

Iparraguirre-Villanueva¹,

Epifanía-Huerta²,

Torres-Ceclén³

et al. 2023

IJACSA

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Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.

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Section: J Model Training and Testingmentioning

confidence: 99%

“…Using features associated with cancer cell imaging, breast cancer can be predicted using ML models. This field of action is in constant development from two deans to after [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Prediction using Machine Learning Models

Iparraguirre-Villanueva¹,

Epifanía-Huerta²,

Torres-Ceclén³

et al. 2023

IJACSA

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“…ML has been applied in drug research and development (22), robotic surgery and decision-making (23), and imaging diagnosis (24), and multiple current studies have pointed out that ML is vitally significant in cancer research, especially cancers of the lung, colorectum, and prostate (14,(25)(26)(27). ML has also attracted widespread attention for BRCA diagnosis and management, such as BRCA tumor identification (28), BRCA neoadjuvant efficacy prediction (29), and BRCA medical imaging analyses (radiomics analysis and histopathological image analysis) (30,31).…”

Section: What Is the Implication And What Should Change Now?mentioning

confidence: 99%

Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis

Zhang¹,

Li²,

Bian³

et al. 2022

Ann Transl Med

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Background: In the era of precision therapy, early classification of breast cancer (BRCA) molecular subtypes has clinical significance for disease management and prognosis. We explored the accuracy of machine learning (ML) models for early classification of BRCA molecular subtypes through a systematic review of the literature currently available. Methods: We retrieved relevant studies published in PubMed, EMBASE, Cochrane, and Web of Science until 15 April 2022. A prediction model risk of bias assessment tool (PROBAST) was applied for the assessment of risk of bias of a genomics-based ML model, and the Radiomics Quality Score (RQS) was simultaneously used to evaluate the quality of this radiomics-based ML model. A random effects model was adopted to analyze the predictive accuracy of genomics-based ML and radiomics-based ML for Luminal A, Luminal B, Basal-like or triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2). The PROSPERO of our study was prospectively registered (CRD42022333611).Results: Of the 38 studies were selected for analysis, 14 ML models were based on gene-transcriptomic, with only 4 external validations; and 43 ML models were based on radiomics, with only 14 external validations. Meta-analysis results showed that c-statistic values of the ML based on radiomics for the identification of BRCA molecular subtypes Luminal A, Luminal B, Basal-like or TNBC, and HER2 were 0.76 [95% confidence interval (

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“…As explained by Rasool et al [14] , normal cells in the breast and other parts of the body grow and divide to form new cells as they are needed. When these normal cells grow old and get damaged, they die and new cells take their place [15] . However sometimes this process goes wrong.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Arika

Mindila

Cheruiyo

2022

Jour. of oncol. res.

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Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.

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Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification

Cited by 14 publications

References 32 publications

Breast Cancer Prediction using Machine Learning Models

Breast Cancer Prediction using Machine Learning Models

Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

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