The purpose of the knowledge-based system is to predict the rehabilitation timeline of a patient in physical therapy for a total knee replacement. All patients have various attributes that contribute to their rehabilitation rate such as: weight, gender, smoking habit, medications, physical ability, or other medical problems. A combination of any one or several of these attributes will affect the recovery process. The proposed FRTP (Fuzzy Rehabilitation Timeline Predictor) is a fuzzy data mining model that can predict the recovery length of a patient in physical therapy for a total knee replacement and provide feedback to experts for revision of the physical therapy plans to meet the recovery goal. Using the FRTP, an approximate timeline for a patient can be predicted, thus creating more insight into the healing process. The process of analyzing patient data, predicting the number of weeks for the maximum healing result, adaptation of a different recovery plan based on our research prototype using fuzzy logic in database systems to maximize the recovery period, is a very interesting and important component for the patient, health insurance companies, medical clinics, and physicians. This research paper presents a methodology to analyze and mine the data using a web based application (Web Fuzzy Data Mining) and fuzzy calculus to perform data mining and predicting the best possible plan for a faster recovery.
Date of Degree
Master of Science (MS)
Computer Information Science
Science, Engineering and Technology
Azarbod, R. (2011). Determining a patient recovery from a total knee replacement using fuzzy logic and active databases. [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/68/
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