Scientific Reports (Jun 2021)

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

  • Pratyusha Rakshit,
  • Onintze Zaballa,
  • Aritz Pérez,
  • Elisa Gómez-Inhiesto,
  • Maria T. Acaiturri-Ayesta,
  • Jose A. Lozano

DOI
https://doi.org/10.1038/s41598-021-91580-x
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 13

Abstract

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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.