Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making. Academic Article uri icon

Overview

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.

publication date

  • May 27, 2022

Identity

PubMed Central ID

  • PMC9221649

Scopus Document Identifier

  • 85097136935

Digital Object Identifier (DOI)

  • 10.3390/healthcare8020111

PubMed ID

  • 35741136

Additional Document Info

volume

  • 12

issue

  • 6