On the Need of Interpretability for Biomedical Applications: Using Fuzzy Models for Lung Cancer Prediction with Liquid Biopsy

Potie, Nicolas; Giannoukakos, Stavros; Hackenberg, Michael; Fernández, Alberto

doi:10.1109/fuzz-ieee.2019.8858976

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…Additionally, it is possible to identify which rules generated the firing strengths (line 8 of Algorithm 1), making possible the generation of a textual explanation for each of the endpoints of the type-reduced set, similar to what can already be done for the outputs of T1 FLS (e.g. [5], [6]).…”

Section: Real-world Applicationmentioning

confidence: 99%

“…The rule-based structure together with the use of linguistic labels [4], allow for the creation of fuzzy logic systems (FLS) that not only give reliable predictions in AI tasks but also have a high level of understandability for both an expert and nonexpert audience. For this reason, FL represents a valuable tool in XAI which has already been successfully applied in some real-world problems [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems

DrAlterio

Garibaldi

John

et al. 2021

IEEE Trans. Fuzzy Syst.

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Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 (T1) to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainability, so far, the latter comes at the expense of high computational cost. Specifically, the exhaustive typereduction method for CIT2 Mamdani systems has been shown to be too slow to be used in practical applications and even the current approximation procedure is much slower than modern type-reduction algorithms used for IT2 fuzzy sets. In this paper, a novel type-reduction procedure for CIT2 sets is presented, based on the concept of switch indices. The algorithm is applied on a real-world classification problem and compared to other typereduction approaches used in IT2 and CIT2 systems. In the case studies presented, the new algorithm is significantly faster than the exhaustive and sampling CIT2 approaches while keeping the high level of interpretability of the type-reduction operation that characterizes CIT2 fuzzy sets.

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Section: Real-world Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems

DrAlterio

Garibaldi

John

et al. 2021

IEEE Trans. Fuzzy Syst.

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“…Several interpretability methods have been proposed over the last few years. Some remarkable approaches are agnostic models that can explain deep learning predictions and used to explain cancer diagnosis through images Palatnik de Sousa et al (2019) and intrinsically interpretable models, such as a fuzzy model that can build a rule set to predict lung cancer with liquid biopsy variables Potie et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for COVID-19 Diagnosis Through Clinical Variables

Thimoteo

Vellasco

Amaral

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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This work proposes an interpretable machine learning approach to diagnosesuspected COVID-19 cases based on clinical variables. Results obtained for the proposed models have F-2 measure superior to 0.80 and accuracy superior to 0.85. Interpretation of the linear model feature importance brought insights about the most relevant features. Shapley Additive Explanations were used in the non-linear models. They were able to show the difference between positive and negative patients as well as offer a global interpretability sense of the models.

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“…In many published articles, e.g. [7]- [9], it is possible to see how the rule-based structure, together with the use of linguistic labels, can be used to provide an explanation in natural language for each of the classifications produced by the systems.…”

Section: Introductionmentioning

confidence: 99%

Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI)

D'Alterio¹,

Garibaldi²,

John³

2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to create models that are able to produce explanations in natural language for each of the classifications they make. So far, however, designing systems that make use of interval type-2 (IT2) fuzzy logic and also give explanations for their outputs has been very challenging, partially due to the presence of the type-reduction step. In this paper, it will be shown how constrained interval type-2 (CIT2) fuzzy sets represent a valid alternative to conventional interval type-2 sets in order to address this issue. Through the analysis of two case studies from the medical domain, it is shown how explainable CIT2 classifiers are produced. These systems can explain which rules contributed to the creation of each of the endpoints of the output interval centroid, while showing (in these examples) the same level of accuracy as their IT2 counterpart.

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On the Need of Interpretability for Biomedical Applications: Using Fuzzy Models for Lung Cancer Prediction with Liquid Biopsy

Cited by 9 publications

References 22 publications

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems

Interpretable Machine Learning for COVID-19 Diagnosis Through Clinical Variables

Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI)

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