Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

Priya, None R. S. Padma; Vadivu, P. Senthil

doi:10.53730/ijhs.v6ns5.8976

Cited by 1 publication

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study stressed the significance of early detection in improving breast cancer prognosis and survival. They emphasized using classification algorithms to develop models capable of reliably classifying breast cancer as malignant or benign [ 7 ]. Another study presented successful outcomes by employing refined machine learning algorithms, which led to greater skills in less invasive predictive medicine and improved treatment options for breast cancer [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models

Devi,

Kaul Ghanekar,

Pande

et al. 2024

Asian Pac J Cancer Prev

View full text Add to dashboard Cite

Background &Objective: Carcinoma of the breast is one of the major issues causing death in women, especially in developing countries. Timely prediction, detection, diagnosis, and efficient therapies have become critical to reducing death rates. Increased use of artificial intelligence, machine, and deep learning techniques create more accurate and trustworthy models for predicting and detecting breast cancer. This study aims to examine the effectiveness of several machine and modern deep learning models for prediction and diagnosis of breast cancer. Methods: This research compares traditional machine learning classification methods to innovative techniques that use deep learning models. Established usual classification models such as k-Nearest Neighbors (kNN), Gradient Boosting, Support Vector Machine (SVM), Neural Network, CN2 rule inducer, Naive Bayes, Stochastic Gradient Descent (SGD), and Tree, and deep learning models such as Neural Decision Forest and Multilayer Perceptron used. The investigation, which was carried out using the Orange and Python tools, evaluates their diagnostic effectiveness in breast cancer detection. The evaluation uses UCI's publicly accessible Wisconsin Diagnostic Data Set, enabling transparency and accessibility in the study approach. Result: The mean radius ranges from 6.981 to 28.110, while the mean texture runs from 9.71 to 39.28 in malignant and benign cases. Gradient boosting and CN2 rule inducer classifiers outperform SVM in accuracy and sensitivity, whereas SVM has the lowest accuracy and sensitivity at 88%. The CN2 rule inducer classifier achieves the greatest ROC curve score for benign and malignant breast cancer datasets, with an AUC score of 0.98%. MLP displays distinguish positive and negative classes, with a higher AUC-ROC of 0.9959. with accuracy of 96.49%, precision of 96.57%, recall of 96.49%, and an F1-Score of 96.50%. Conclusion: Among the most commonly used classifier models, CN2 rule and GB performed better than other models. However, MLP from deep learning produced the greatest overall performance.

show abstract

Section: Introductionmentioning

confidence: 99%