Early predictive model for breast cancer classification using blended ensemble learning

Breast cancer is one of the most common cancers among women’s worldwide, and it is a fact that most of the cases are discovered late. Several researchers have examined the prediction of breast cancer. Breast cancer poses a significant hazard to women. The deficiency of reliable predictive models rea...

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Veröffentlicht in:International journal of system assurance engineering and management Jg. 15; H. 1; S. 188 - 197
Hauptverfasser: Mahesh, T. R., Vinoth Kumar, V., Vivek, V., Karthick Raghunath, K. M., Sindhu Madhuri, G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New Delhi Springer India 01.01.2024
Springer Nature B.V
Schlagworte:
Accuracy
Breast cancer
Cancer therapies
Classification
Data mining
Datasets
Decision trees
Disease
Engineering
Engineering Economics
Ensemble learning
Fuzzy sets
Genetic algorithms
Logistics
Machine learning
Mammography
Marketing
Medical diagnosis
Medical prognosis
Neural networks
Organization
Original Article
Patients
Performance measurement
Prediction models
Quality Control
Reliability
Safety and Risk
Support vector machines
Womens health
United States > US
Wisconsin
Accuracy
Precision
Machine learning
Prediction
Breast cancer
Recall
Diagnosis
Detection system
ISSN:0975-6809, 0976-4348
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Zusammenfassung:Breast cancer is one of the most common cancers among women’s worldwide, and it is a fact that most of the cases are discovered late. Several researchers have examined the prediction of breast cancer. Breast cancer poses a significant hazard to women. The deficiency of reliable predictive models really makes it challenging for clinicians to devise a treatment strategy that will help patients live longer. An automatic illness detection system assists medical personnel in diagnosing disease and provides a reliable, efficient and quick reaction while also lowering the danger of death. A Blended ensemble learning, which is an innovative approach, has been utilized for the classification of breast cancer and this model performs effectively for the base classifier in the prediction analysis. The performance of five machine learning techniques, namely support vector machine, K-nearest neighbors, decision tree Classifier, random forests, and logistic regression, are used as base learners in blended ensemble model. All the incorporated base learners (individually) and the final outcome of the Ensemble Learning are being compared in this study against several performance metrics namely accuracy, recall, precision and f1-score for the early prediction of Breast Cancer. There is a 98.14 percent noticeable improvement with the Ensemble Learning model compared to the basic learners.
Bibliographie:ObjectType-Article-1
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ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-022-01696-0