An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and Bayesian Optimization

P, Pratheep Kumar; Bai, Mary Amala; Nair, Geetha G.

doi:10.1016/j.bspc.2021.102682

Cited by 39 publications

(13 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the work in ( 34 ) has proposed a BC diagnosing classifier by using Kernel Neutrosophic c-Means Clustering as a feature weighting and random decision forest with Bayesian optimization as a classifier. The proposed system has been evaluated based on the WDBC database.…”

Section: Related Workmentioning

confidence: 99%

Breast cancer diagnosis using the fast learning network algorithm

et al. 2023

View full text Add to dashboard Cite

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.

show abstract

Section: Related Workmentioning

confidence: 99%

Breast cancer diagnosis using the fast learning network algorithm

et al. 2023

View full text Add to dashboard Cite

show abstract

“…High performance is aimed at the machine learning concept by adjusting the hyperparameter values of the classi er algorithms. There are many studies [19][20][21][22][23][24][25] in the literature dealing with this issue. In this study, Randomize Search cross-validation and bayesian search cross-validation methods were used for the optimal combination of SVM hyperparameters.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Akyol

2023

Preprint

View full text Add to dashboard Cite

Coronary artery disease outstands health problem that causes high mortality in the world population. This disease brings with it fateful problems such as heart attack and heart failure in patients with cardiovascular problems. Early diagnosis of coronary artery disease is essential for the timely administration of the right treatment and reduction of mortality. Angiography is the most preferred method for CAD detection. However, the complications and costs of this method have led researchers to forage alternative methods through machine learning algorithms. By developing a machine learning model with high generalization ability, prediction errors can be minimized. Thus, these models could potentially be useful for specialist physicians in the effective detection of coronary artery disease. The main focus of this study is to perform coronary artery disease detection with improved support vector machines. k-fold cross-validation experiments were performed on the Z-Alizadeh Sani dataset to evaluate the performance of the models. According to the results obtained, support vector machines with randomized search cross-validation provided the best performance when compared to other models. 87.102% average accuracy, 91.176% average sensitivity, 90.852% average precision, 76.996% average specificity, and also 8.824% average false negative rate obtained by 5-fold cross-validation competes with the known approaches in the literature.

show abstract

“…Many scientists have used ML and DL methods to create breast cancer prediction models and strategies. ML algorithms such as SVM, 23 RF, 24 LR, decision tree 25 (C4.5), K ‐NN, and so on. Using resources, like the standard of excellence for cancer registries data set, mammogram pictures as a data set, the Wisconsin data set, and hospital records, numerous researchers have conducted breast cancer research 26 .…”

Section: Scientific Literaturementioning

confidence: 99%

Breast cancer: A comparative review for breast cancer detection using machine learning techniques

Khan,

Singh,

Sultana

et al. 2023

Cell Biochemistry & Function

View full text Add to dashboard Cite

Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer. The main goal of this review is to explore various techniques of machine learning algorithms to examine high accuracy and early detection of breast cancer for the safe health of women.

show abstract

An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and Bayesian Optimization

Cited by 39 publications

References 20 publications

Breast cancer diagnosis using the fast learning network algorithm

Breast cancer diagnosis using the fast learning network algorithm

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Breast cancer: A comparative review for breast cancer detection using machine learning techniques

Contact Info

Product

Resources

About