Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool

How good is crude MDL for solving the bias-variance dilemma? An empirical investigation based on Bayesian networks.

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size

LaDonna (House) Moore poses with others on couch

LaDonna (House) Moore (top left) poses with others on couch, c.1980\u27shttps://digitalcommons.georgefox.edu/gfu_photos_1980_1984/1103/thumbnail.jp

Exhaustive evaluation of BIC (low-entropy values).

<p>Exhaustive evaluation of BIC (low-entropy values).</p

Gold-standard Network.

<p>Gold-standard Network.</p

Graph with best value (AIC, MDL, BIC - random distribution).

<p>Graph with best value (AIC, MDL, BIC - random distribution).</p

Graph with minimum AIC2 and MDL2 value.

<p>Graph with minimum AIC2 and MDL2 value.</p

Same values for k and different values for MDL; different values for k and same values for MDL.

<p>Same values for k and different values for MDL; different values for k and same values for MDL.</p

Expansion and evaluation algorithm.

<p>Expansion and evaluation algorithm.</p