ArticleDiagnostics (Basel, Switzerland)2022
Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Comparative Evaluation of Deep Learning Models for Diagnosis of Helminth Infections.Journal of personalized medicine · 2025Article
- Article
- Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI.Scientific reports · 2024Article
- Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest.Diagnostics (Basel, Switzerland) · 2024Article
- Assessment of UTI Diagnostic Techniques Using the Fuzzy-PROMETHEE Model.Diagnostics (Basel, Switzerland) · 2023Article
- An intuitionistic approach for the predictability of anti-angiogenic inhibitors in cancer diagnosis.Scientific reports · 2023Article
- COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework.Diagnostics (Basel, Switzerland) · 2023Article
- An Alternative Diagnostic Method forDiagnostics (Basel, Switzerland) · 2022Article
- Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
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Abstract
On average, breast cancer kills one woman per minute. However, there are more reasons for optimism than ever before. When diagnosed early, patients with breast cancer have a better chance of survival. This study aims to employ a novel approach that combines artificial intelligence and a multi-criteria decision-making method for a more robust evaluation of machine learning models. The proposed machine learning techniques comprise various supervised learning algorithms, while the multi-criteria decision-making technique implemented includes the Preference Ranking Organization Method for Enrichment Evaluations. The Support Vector Machine, having achieved a net outranking flow of 0.1022, is ranked as the most favorable model for the early detection of breast cancer. The net outranking flow is the balance between the positive and negative outranking flows. This indicates that the higher the net flow, the better the alternative. K-nearest neighbor, logistic regression, and random forest classifier ranked second, third, and fourth, with net flows of 0.0316, -0.0032, and -0.0541, respectively. The least preferred alternative is the naive Bayes classifier with a net flow of -0.0766. The results obtained in this study indicate the use of the proposed method in making a desirable decision when selecting the most appropriate machine learning model. This gives the decision-maker the option of introducing new criteria into the decision-making process.
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