Liver fibrosis diagnosis support using the Dempster–Shafer theory extended for fuzzy focal elements

Porębski, Sebastian; Porwik, Piotr; Straszecka, Ewa; Orczyk, Tomasz

doi:10.1016/j.engappai.2018.09.004

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…Their proposed approach proved to be efficient and feasible as it improved performance by reducing the uncertainty caused due to subjectivity. Authors in (Porebski et al, 2018) proposed a diagnosis support method and a rule selection both by applying fuzzy set and Dempster-Shafer theories. They used their model to diagnose liver fibrosis.…”

Section: Related Work Based On Dstmentioning

confidence: 99%

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Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Section: Related Work Based On Dstmentioning

confidence: 99%

“…Authors in (Porebski et al 2018) proposed a diagnosis support method and a rule selection both by applying fuzzy set and Dempster-Shafer theories. They utilized their model to diagnose liver fibrosis.…”

Section: Related Work Based On Dstmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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“…Gao and Deng 17 associated the quantum model with evidence theory and proposed the corresponding mass functions. Not only the mass function but also the structure of BPA has been widely researched 30 . Yager 31 proposed the fuzzy rule bases on generalized belief structures.…”

Section: Introductionmentioning

confidence: 99%

A new structure of the focal element in object recognition

Long

Peng

Deng

2021

Int J of Intelligent Sys

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The research on uncertainty is a hot spot in artificial intelligence, especially in actual applications. Most research focus on measuring the uncertainty between evidence, ignoring the structure of evidence. When a sample has multiple features, the traditional structure of the focal element cannot handle and display the uncertainty between multiple features directly. Hence, we proposed a new structure of the focal element considering multiple features into one focal element and provided the corresponding basic probability assignment calculation. To apply the new structure in practices, we provided a new object recognition method combining the fractal feature and credible evidence. The new method can improve the robustness of the algorithm in a noisy environment. In the end, some experiments have illustrated the validity and correctness of the proposed method.

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Improving the Quality of Clustering-Based Diagnostic Rules by Lowering Dimension of the Cluster Prototypes

Porębski¹,

Straszecka²

2019

Advances in Intelligent Systems and Computing

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Liver fibrosis diagnosis support using the Dempster–Shafer theory extended for fuzzy focal elements

Cited by 12 publications

References 36 publications

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

A new structure of the focal element in object recognition

Improving the Quality of Clustering-Based Diagnostic Rules by Lowering Dimension of the Cluster Prototypes

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