Prediction of patient choice tendency in medical decision-making based on machine learning algorithm

Bibliographic Data

ID22072966
AuthorsYuWen Lyu (0000-0003-1562-3350, Guangzhou Medical University), Qian Xu (0000-0001-8130-0846, Guangzhou Medical University), Zhenchao Yang (0000-0001-7605-0301, Guangzhou Medical University), Junrong Liu (0000-0002-3466-8485, Guangzhou Medical University, corresponding author)
Year2023
Volume11
Pages1087358-1087358
Publication date2023-02-24
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1087358
PMID36908484
OpenAlexW4321788671
LanguageEN
Citations received3
References cited45

Objective: Machine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions. Method: Patient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared. Results: The accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94. Conclusion: Among the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making

Algorithm · Clinical decision making · Decision tree · Decision tree learning · Family medicine · Machine learning · Medical decision making · Statistical classification · Support vector machine · Advanced Technologies in Various Fields · Artificial Intelligence in Healthcare · Computer Science · Machine Learning in Healthcare · Medicine · Artificial Intelligence

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