Significance of nuclear morphometry in benign and malignant breast aspirates

Narasimha, Aparna; Vasavi, B; Kumar, ML Harendra

doi:10.4103/2229-516x.112237

Cited by 18 publications

(16 citation statements)

References 9 publications

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“…Among these rules, size, shape, and texture features occur more often than other features indicating these features are more important than other features in deciding breast cancer. This result is similar to conclusion made in [ 16 ] and [ 17 ].…”

Section: Resultssupporting

confidence: 93%

Learning accurate and interpretable models based on regularized random forests regression

et al. 2014

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BackgroundMany biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance.MethodsIn this study, we focus on regression problems for biological data where target outcomes are continuous. In general, models constructed from linear regression approaches are relatively easy to interpret. However, many practical biological applications are nonlinear in essence where we can hardly find a direct linear relationship between input and output. Nonlinear regression techniques can reveal nonlinear relationship of data, but are generally hard for human to interpret. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features.ResultsWe tested the approach on some biological data sets. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression.ConclusionIt demonstrates high potential in aiding prediction and interpretation of nonlinear relationships of the subject being studied.

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Section: Resultssupporting

confidence: 93%

Learning accurate and interpretable models based on regularized random forests regression

et al. 2014

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“…the shape is more regular; on the other hand, the class becomes malignant with an increase in the radius, area and / or perimeter (note that these three features are related, thus a change on at least two of them is required). These results are in line with what previously reported in the literature ( Abdalla et al, 2008;Narasimha, Vasavi, & Kumar, 2013;Wittekind & Schulte, 1987 ). In the case of the wines data set, the most important element allowing to increase the quality of a lesser wine is the reduction of its volatile acidity.…”

Section: Classification Models Analysissupporting

confidence: 93%

Fostering interpretability of data mining models through data perturbation

Belkoura

Zanin

LaTorre

2019

Expert Systems with Applications

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With the widespread adoption of data mining models to solve real-world problems, the scientific community is facing the need of increasing their interpretability and comprehensibility. This is especially relevant in the case of black box models, in which inputs and outputs are usually connected by highly complex and nonlinear functions; in applications requiring an interaction between the user and the model; and when the machine's solution disagrees with the human experience. In this contribution we present a new methodology that allows to simplify the process of understanding the rules behind a classification model, even in the case of black box ones. It is based on the perturbation of the features describing one instance, and on finding the minimal variation required to change the forecasted class. It thus yields simplified rules describing under which circumstances would the solution have been different, and allows to compare these with the human expectation. We show how such methodology is well defined, model-agnostic, easy to implement and modular; and demonstrate its usefulness with several synthetic and real-world data sets.

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“…An intermediate MNA value of 107.03±6.60 was observed in borderline cases which is comparable to study by Narasimha et al (6) (Table III). …”

Section: Discussionsupporting

confidence: 80%

Significance of morphometric parameters in the categorization of breast lesions on cytology

Yadav¹,

Gill²,

Srivastava³

et al. 2015

TJPATH

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Significance of nuclear morphometry in benign and malignant breast aspirates

Cited by 18 publications

References 9 publications

Learning accurate and interpretable models based on regularized random forests regression

Learning accurate and interpretable models based on regularized random forests regression

Fostering interpretability of data mining models through data perturbation

Significance of morphometric parameters in the categorization of breast lesions on cytology

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