Determining a Patient Recovery from a Total Knee Replacement Using Fuzzy Logic and Active Databases

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

A Web Based Fuzzy Data Mining Using Combs Inference Method And Decision Predictor

Fuzzy logic has become a very popular method of reasoning a system with approximate input system instead of a precise one. When qualitative variables are used to determine the decisions then we have to create some specific functions where the membership values of the input can be any number between 0 to 1 instead of 1 or 0 which is used in binary logic. When number of input attribute increases it the combinatorial rules increases exponentially, and diminishes performance of the system. The problem is generally known as “combinatorial rule explosion”. The Information Technology Department of Minnesota State University, Mankato has been developing a system to analyze historical data and mining. The research paper presents a methodology to reduce the number of rules used in the application and creating a data prediction system using partial incomplete data set