Risk Factor Analysis to Patient Based on Fuzzy Logic Control System

Fuzzy logic has proved in this paper, a medical fuzzy data is introduced in order to help users in providing accurate information when there is inaccuracy. Inaccuracy in data represents imprecise or vague values (like the words use in human conversation) or uncertainty in using the available information required for decision making handle the uncertainty of critical risk for human health. In this paper involved to diagnosis the health risk which is related to Blood Pressure, Pulse rate and Kidney function. The confusing nature of the symptoms makes it difficult for physicians using psychometric assessment tools alone to determine the risk of the disease. This paper describes research results in the development of a fuzzy driven system to determine the risk levels of health for the patients. The system is implemented and simulated using MATLAB fuzzy tool box. Keywords—Fuzzy logic control system, Risk analysis, Sugeno-type, Fuzzy Inference System, MATLAB Tool, ANFIS, Defuzzification INTRODUCTION In the fields of medicine area diagnosis, treatment of illnesses and patient pursuit has highly increased. Despite the fact that these fields, in which the computers are used, have very high complexity and uncertainty and the use of intelligent systems such as fuzzy logic, artificial neural network and genetic algorithm have been developed. In the other word, there exists no strict boundary between what is Healthy and what is diseased, thus distinguish is uncertain and vague [2]. Having so many factors to analyze to diagnose the heart disease of a patient makes the physician’s job difficult. So, experts require an accurate tool that considering these risk factors and show certain result in uncertain term. Motivated by the need of such an important tool, in this study, it designed an expert system to diagnose the heart disease. The designed expert system based on Fuzzy Logic. This fuzzy control system that deals with diagnosis has been implemented in MATLAB Tool. In this paper introduced fuzzy control system to design fuzzy rule base to analyse the risk factor of patient health and the rule viewed by surface view. FUZZY INFERENCE SYSTEM In this study, it present a Fuzzy control System for the diagnosis risk factor from the collection of Blood pressure value, pulse rate and kidney function are used as a several parameter to determine risk analysis by fuzzy rule respectively. A typical architecture of FLC is shown below, which comprises of four principal comprises such as a fuzzifier, a fuzzy rule base, inference engine, and defuzzifier. In fuzzy inference process, Blood pressure value, pulse rate and kidney function value are the inputs to transmit for making decision on basis of pattern discerned. Also involves all pieces that are described in Membership Functions and If-Then Rules. METHODOLOGY BACKGROUND INPUT DATA Medical diagnosis is a complicated task that requires operating accurately and efficiently. Such complicated databases are supported of uncertain information is called a fuzzy database [7] [8]. Neuro-adaptive learning techniques provide to learn information about a data set for modeling the operation in procedure. Using a given input/output data set, the toolbox function adaptive neurofuzzy inference system (ANFIS) constructs a fuzzy inference system (FIS) whose membership function parameters are adjusted using either a back propagation algorithm. The inputs of linguistic variable are put into the measurement for performing to the Sugeno member function method and assigned the rule base refer the Table I, Table II, and Table III using If.. Then rule insert into the tool to analyse the risk factor of patient. Kidney function was measured by several classified Glomerular Filtration Rate (GFR) such as Normal, problem started GFR, Below GFR, Moderate GFR, Below Moderate GFR, Damage GFR and Kidney failure. Blood pressure (BP) values also International Journal of Engineering Research and General Science Volume 2, Issue 5, August-September, 2014 ISSN 2091-2730 186 www.ijergs.org classified by different ranges such as Low normal, Low BP, Very Low BP, Extreme Low BP, Danger Low BP, Very Danger Low BP, Danger too Low BP, Normal BP, High Normal BP, Border line BP, High BP, Very High BP, Extreme very High BP, Very danger High BP. Pulse values are derived from systolic and diastolic Blood pressure value. Such Blood pressure values to be analyzing to the kidney function for determine the risk factor. TABLE I. Analysis the variable in Rule Base TABLE II. Analysis the variable in Rule Base (Contd) Cases Comment Blood Pressure 60-40 Very Danger Low BP 50-30 Danger Too Low BP 120-80 Normal BP 130-85 High Normal BP 140-90 Border Line BP Kidney Function [Glomerular Filtration Rate] Normal (> 90) Very Danger Low BP ++ Low BP ++ Normal BP High Normal BP Border Line BP Below GFR (80-89) Moderate GFR (45-59) Very Danger Low Bp + Low BP + High Normal BP + Below Moderate GFR (30-44) Damage GFR (15-29) Very Danger Low BP Low BP High Normal BP ++ Kidney Failure GFR<15 Cases Comment Blood Pressure 115-75 Low Norma l 100-65 Low BP 90-60 Very Low BP 80-55 Extreme Low BP 70-45 Danger Low BP Kidney Function [Glomerular Filtration Rate] Normal (> 90) Low Normal + + Low