A machine learning approach to predict healthcare cost of breast cancer patients

Abstract This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the seq...

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Autores principales:	Pratyusha Rakshit, Onintze Zaballa, Aritz Pérez, Elisa Gómez-Inhiesto, Maria T. Acaiturri-Ayesta, Jose A. Lozano
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f4888c91e9e046edab973f1eea026540
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spelling	oai:doaj.org-article:f4888c91e9e046edab973f1eea0265402021-12-02T17:24:22ZA machine learning approach to predict healthcare cost of breast cancer patients10.1038/s41598-021-91580-x2045-2322https://doaj.org/article/f4888c91e9e046edab973f1eea0265402021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91580-xhttps://doaj.org/toc/2045-2322Abstract This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.Pratyusha RakshitOnintze ZaballaAritz PérezElisa Gómez-InhiestoMaria T. Acaiturri-AyestaJose A. LozanoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pratyusha Rakshit Onintze Zaballa Aritz Pérez Elisa Gómez-Inhiesto Maria T. Acaiturri-Ayesta Jose A. Lozano A machine learning approach to predict healthcare cost of breast cancer patients
description	Abstract This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.
format	article
author	Pratyusha Rakshit Onintze Zaballa Aritz Pérez Elisa Gómez-Inhiesto Maria T. Acaiturri-Ayesta Jose A. Lozano
author_facet	Pratyusha Rakshit Onintze Zaballa Aritz Pérez Elisa Gómez-Inhiesto Maria T. Acaiturri-Ayesta Jose A. Lozano
author_sort	Pratyusha Rakshit
title	A machine learning approach to predict healthcare cost of breast cancer patients
title_short	A machine learning approach to predict healthcare cost of breast cancer patients
title_full	A machine learning approach to predict healthcare cost of breast cancer patients
title_fullStr	A machine learning approach to predict healthcare cost of breast cancer patients
title_full_unstemmed	A machine learning approach to predict healthcare cost of breast cancer patients
title_sort	machine learning approach to predict healthcare cost of breast cancer patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f4888c91e9e046edab973f1eea026540
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A machine learning approach to predict healthcare cost of breast cancer patients

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