A machine learning approach to predict healthcare cost of breast cancer patients
| Autor: | Elisa Gómez-Inhiesto, Jose A. Lozano, Onintze Zaballa, María Teresa Acaiturri-Ayesta, Pratyusha Rakshit, Aritz Pérez |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Science MEDLINE Breast Neoplasms 02 engineering and technology Machine learning computer.software_genre Disease cluster Article Machine Learning 03 medical and health sciences 0302 clinical medicine Breast cancer 020204 information systems Early prediction Health care 0202 electrical engineering electronic engineering information engineering medicine Cluster Analysis Electronic Health Records Humans 030212 general & internal medicine Multidisciplinary Markov chain business.industry Health Care Costs Health care economics medicine.disease Markov Chains Models Economic Mean absolute percentage error Medicine Healthcare cost Female Artificial intelligence business computer |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-021-91580-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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