On the information hidden in a classifier distribution

Abstract Classification tasks are a common challenge to every field of science. To correctly interpret the results provided by a classifier, we need to know the performance indices of the classifier including its sensitivity, specificity, the most appropriate cut-off value (for continuous classifier...

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Autores principales:	Farrokh Habibzadeh, Parham Habibzadeh, Mahboobeh Yadollahie, Hooman Roozbehi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6337f968ed55456f97c7e88bd7135a15
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spelling	oai:doaj.org-article:6337f968ed55456f97c7e88bd7135a152021-12-02T14:12:10ZOn the information hidden in a classifier distribution10.1038/s41598-020-79548-92045-2322https://doaj.org/article/6337f968ed55456f97c7e88bd7135a152021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79548-9https://doaj.org/toc/2045-2322Abstract Classification tasks are a common challenge to every field of science. To correctly interpret the results provided by a classifier, we need to know the performance indices of the classifier including its sensitivity, specificity, the most appropriate cut-off value (for continuous classifiers), etc. Typically, several studies should be conducted to find all these indices. Herein, we show that they already exist, hidden in the distribution of the variable used to classify, and can readily be harvested. An educated guess about the distribution of the variable used to classify in each class would help us to decompose the frequency distribution of the variable in population into its components—the probability density function of the variable in each class. Based on the harvested parameters, we can then calculate the performance indices of the classifier. As a case study, we applied the technique to the relative frequency distribution of prostate-specific antigen, a biomarker commonly used in medicine for the diagnosis of prostate cancer. We used nonlinear curve fitting to decompose the variable relative frequency distribution into the probability density functions of the non-diseased and diseased people. The functions were then used to determine the performance indices of the classifier. Sensitivity, specificity, the most appropriate cut-off value, and likelihood ratios were calculated. The reference range of the biomarker and the prevalence of prostate cancer for various age groups were also calculated. The indices obtained were in good agreement with the values reported in previous studies. All these were done without being aware of the real health status of the individuals studied. The method is even applicable for conditions with no definite definitions (e.g., hypertension). We believe the method has a wide range of applications in many scientific fields.Farrokh HabibzadehParham HabibzadehMahboobeh YadollahieHooman RoozbehiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Farrokh Habibzadeh Parham Habibzadeh Mahboobeh Yadollahie Hooman Roozbehi On the information hidden in a classifier distribution
description	Abstract Classification tasks are a common challenge to every field of science. To correctly interpret the results provided by a classifier, we need to know the performance indices of the classifier including its sensitivity, specificity, the most appropriate cut-off value (for continuous classifiers), etc. Typically, several studies should be conducted to find all these indices. Herein, we show that they already exist, hidden in the distribution of the variable used to classify, and can readily be harvested. An educated guess about the distribution of the variable used to classify in each class would help us to decompose the frequency distribution of the variable in population into its components—the probability density function of the variable in each class. Based on the harvested parameters, we can then calculate the performance indices of the classifier. As a case study, we applied the technique to the relative frequency distribution of prostate-specific antigen, a biomarker commonly used in medicine for the diagnosis of prostate cancer. We used nonlinear curve fitting to decompose the variable relative frequency distribution into the probability density functions of the non-diseased and diseased people. The functions were then used to determine the performance indices of the classifier. Sensitivity, specificity, the most appropriate cut-off value, and likelihood ratios were calculated. The reference range of the biomarker and the prevalence of prostate cancer for various age groups were also calculated. The indices obtained were in good agreement with the values reported in previous studies. All these were done without being aware of the real health status of the individuals studied. The method is even applicable for conditions with no definite definitions (e.g., hypertension). We believe the method has a wide range of applications in many scientific fields.
format	article
author	Farrokh Habibzadeh Parham Habibzadeh Mahboobeh Yadollahie Hooman Roozbehi
author_facet	Farrokh Habibzadeh Parham Habibzadeh Mahboobeh Yadollahie Hooman Roozbehi
author_sort	Farrokh Habibzadeh
title	On the information hidden in a classifier distribution
title_short	On the information hidden in a classifier distribution
title_full	On the information hidden in a classifier distribution
title_fullStr	On the information hidden in a classifier distribution
title_full_unstemmed	On the information hidden in a classifier distribution
title_sort	on the information hidden in a classifier distribution
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6337f968ed55456f97c7e88bd7135a15
work_keys_str_mv	AT farrokhhabibzadeh ontheinformationhiddeninaclassifierdistribution AT parhamhabibzadeh ontheinformationhiddeninaclassifierdistribution AT mahboobehyadollahie ontheinformationhiddeninaclassifierdistribution AT hoomanroozbehi ontheinformationhiddeninaclassifierdistribution
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On the information hidden in a classifier distribution

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