Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Rasool, Abdur; Bunterngchit, Chayut; Luo, Tiejian; Islam, Md. Ruhul; Qu, Qiang; Jiang, Qingshan

doi:10.3390/ijerph19063211

Cited by 58 publications

(29 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rasool et al [31] developed a model with RFE for the same Coimbra breast dataset. They applied the hold-out validation (80% for training and 20% for testing) method and achieved the highest accuracy up to 76.42% for the polynomial SVM classifier.…”

Section: Study Comparison With Earlier Workmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

View full text Add to dashboard Cite

Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

show abstract

Section: Study Comparison With Earlier Workmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

View full text Add to dashboard Cite

show abstract

“…They ( 17 ) constructed four distinct predictive models and offered data exploratory techniques (DET) to increase breast cancer detection accuracy in this study. Prior to the models, researchers dug deep into four-layered critical DET, such as feature distribution, correlation, removal, and hyperparameter optimization, to find the most robust feature categorization into malignant and benign classifications.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

show abstract

“…However, these traditional breast cancer detection techniques suffer from various setbacks. For instance, biopsy techniques prove to be painful to patients [14] , while mammogram suffers from noise and is not reliable for detection of breast cancer. As discussed by Moloney et al [24] , mammography to digital breast cancer exploit attenuation of X-rays as they pass through breast tissue and it remains a good standard investigation of symptomatic women aged 40 years.…”

Section: Conventional Breast Cancer Diagnosis Techniquesmentioning

confidence: 99%

“…The malignant tumor may begin in the cells of the breast, which then spreads to the surrounding tissues [11][12][13] . 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] .…”

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.

View full text Add to dashboard Cite

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.

show abstract

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Cited by 58 publications

References 44 publications

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Breast Cancer Detection and Classification Empowered With Transfer Learning

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

Contact Info

Product

Resources

About