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.


Mahbubyr Syed

Committee Member

Hamed Sallam

Committee Member

Cyrus Azarbod

Date of Degree




Document Type



Master of Science (MS)


Computer Information Science


Science, Engineering and Technology

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License



Rights Statement

In Copyright