Sub-clustering based recommendation system for stroke patient: Identification of a specific drug class for a given patient.

ABSTRACT: Stroke is one of the leading causes of death worldwide. Previous studies have explored machine learning techniques for early detection of stroke patients using content-based recommendation systems. However, these models often struggle with timely detection of medications, which can be critical for patient management and decision-making regarding the prescription of new drugs. In this study, we developed a content-based recommendation model using three machine learning algorithms: Gaussian Mixture Model (GMM), Affinity Propagation (AP), and K-Nearest Neighbors (KNN), to aid Healthcare Professionals (HCP) in quickly detecting medications based on the symptoms of a patient with stroke. Our model focused on three classes of drugs: antihypertensive, anticoagulant, and fibrate. Each machine lear

SUBMITTER: Ceskoutse RFT 

PROVIDER: S-EPMC10981530 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature