Probabilistic machine learning for breast cancer classification

Anastasia-Maria Leventi-Peetz; Kai Weber; Anastasia-Maria Leventi-Peetz; Kai Weber

doi:10.3934/mbe.2023029

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 624-655. doi: 10.3934/mbe.2023029

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Research article

Probabilistic machine learning for breast cancer classification

Anastasia-Maria Leventi-Peetz ^{1
,
,},
Kai Weber ²

1.
Federal Office for Information Security, Postfach 200363, 53133 Bonn, Germany
2.
inducto Daten- und Informationssysteme GmbH, 84405 Dorfen, Germany

Academic Editor: Yang Kuang

Received: 31 July 2022 Revised: 29 September 2022 Accepted: 08 October 2022 Published: 13 October 2022

A probabilistic neural network has been implemented to predict the malignancy of breast cancer cells, based on a data set, the features of which are used for the formulation and training of a model for a binary classification problem. The focus is placed on considerations when building the model, in order to achieve not only accuracy but also a safe quantification of the expected uncertainty of the calculated network parameters and the medical prognosis. The source code is included to make the results reproducible, also in accordance with the latest trending in machine learning research, named Papers with Code. The various steps taken for the code development are introduced in detail but also the results are visually displayed and critically analyzed also in the sense of explainable artificial intelligence. In statistical-classification problems, the decision boundary is the region of the problem space in which the classification label of the classifier is ambiguous. Problem aspects and model parameters which influence the decision boundary are a special aspect of practical investigation considered in this work. Classification results issued by technically transparent machine learning software can inspire more confidence, as regards their trustworthiness which is very important, especially in the case of medical prognosis. Furthermore, transparency allows the user to adapt models and learning processes to the specific needs of a problem and has a boosting influence on the development of new methods in relevant machine learning fields (transfer learning).
- Bayesian neural networks,
- decision boundary,
- explainable artificial intelligence,
- machine learning,
- model uncertainty,
- posterior predictive check,
- prior probability,
- probabilistic programming,
- scatter plot,
- variational posterior distribution
Citation: Anastasia-Maria Leventi-Peetz, Kai Weber. Probabilistic machine learning for breast cancer classification[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 624-655. doi: 10.3934/mbe.2023029

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Abstract

A probabilistic neural network has been implemented to predict the malignancy of breast cancer cells, based on a data set, the features of which are used for the formulation and training of a model for a binary classification problem. The focus is placed on considerations when building the model, in order to achieve not only accuracy but also a safe quantification of the expected uncertainty of the calculated network parameters and the medical prognosis. The source code is included to make the results reproducible, also in accordance with the latest trending in machine learning research, named Papers with Code. The various steps taken for the code development are introduced in detail but also the results are visually displayed and critically analyzed also in the sense of explainable artificial intelligence. In statistical-classification problems, the decision boundary is the region of the problem space in which the classification label of the classifier is ambiguous. Problem aspects and model parameters which influence the decision boundary are a special aspect of practical investigation considered in this work. Classification results issued by technically transparent machine learning software can inspire more confidence, as regards their trustworthiness which is very important, especially in the case of medical prognosis. Furthermore, transparency allows the user to adapt models and learning processes to the specific needs of a problem and has a boosting influence on the development of new methods in relevant machine learning fields (transfer learning).

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