Cited by A Hybrid Model to Predict the Breast Cancer using Stacking and Bagging Model

A Hybrid Model to Predict the Breast Cancer using Stacking and Bagging Model DOI

et al.

Published: Dec. 4, 2023

Breast cancer is a malignant tumor that develops in the cells of breast tissue. one major causes death for women globally. In examination medical data, prediction difficult task. To make decisions and accurately distinguish between benign tumors, physicians pathologists need certain automated technologies. this paper, hybrid ensemble technique (Bagging Stacking) used to predict tumors as tumors. proposed work, subset data created from initial Wisconsin (Diagnostic) Data Set by bootstrapping technique. Each bootstrap dataset train weak learner. The learners are K-Nearest Neighbors (KNN) Random Forest (RF), Decision Tree (DT) Support Vector Machine (SVM). Logistic Regression (LR) Meta Learner. Learner uses predictions its training data. model obtains an accuracy 98.7%, Precision 98.83%, Recall 98.54%, F1 Score 98.68% 0.012% error

Language: Английский

Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models DOI

Umair Arif,

Chunxia Zhang,

Muhammad Waqas Chaudhary

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 14

Published: July 13, 2024

Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There an urgent need develop effective lung prediction model that will help the early diagnosis and save patients from unnecessary treatments. The objective current paper meet extensiveness measure by using collaborative feature selection extraction methods enhance dendritic neural (DNM) comparison traditional machine learning (ML) models with minimum features boost accuracy, precision, sensitivity prediction. Comprehensive experiments on dataset comprising 1000 23 obtained Kaggle. Crucial are identified, proposed method's effectiveness evaluated metrics such as F1 score, sensitivity, specificity, confusion matrix against other ML models. Feature techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), Uniform Manifold Approximation Projection (UMAP) employed optimize performance. DNM accuracy at 96.50%, precision 96.64% 97.45% sensitivity. K-PCA explained 98.50%, 99.42%, 98.84% UMAP elaborated 98%, 98.82%, 98.82% approach showed outstanding performance enhancing model. Highlighting DNM's accurate cancer. These results emphasize potential contribute positively healthcare research providing better predictive outcomes.

Language: Английский

Citations

AI-based hierarchical approach for optimizing breast cancer detection using MammoWave device DOI