Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making

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.

Automated summary

This study proposes a new approach combining artificial intelligence and multi-criteria decision-making for a more robust evaluation of machine learning models for early detection of breast cancer. The Support Vector Machine is ranked as the most favorable model, indicating the effectiveness of the proposed method in decision-making.