{"title":"基于自适应神经模糊接口系统的患者危险因素分析","authors":"M. Mayilvaganan, K. Rajeswari","doi":"10.1109/ICCIC.2014.7238348","DOIUrl":null,"url":null,"abstract":"The proposed methodology involved in this paper, is to diagnosis and analysis the health risk factor which is related to Blood Pressure, Pulse rate and Kidney function by Glomerular Filtration Rate (GFR). The computing techniques can handle two most predominant values such as `True' or `False', `1' or `0', `Black' or `White', but Fuzzy Logic, also handle grey values which occur in between `Black' and `White'. The system consists of 234 combination input fields and one output field. This work focus about Adaptive Neuro Fuzzy Interface System (ANFIS) depends on fuzzy logic controller to diagnose the various level of health risk factor value which is aggregated with Blood Pressure, Pulse Rate and Kidney function based on various Input Parameters. In this paper, Fuzzy Logic circuit was developed with 2's Complement in full adder using the input such as Blood Pressure value taken from Systolic and Diastolic value, Pulse Rate and GFR value. Using the OR gate value, Pulse rate and Blood pressure value are compared with Kidney function and getting the output as risk factor value in efficient manner. The input rule based classifier membership functions are X0, X1, X2. Xn for blood pressure values such as Low, Normal, Very Low, Extreme Low Meds, Very Danger Low, Danger too Low BP, Border Line, Very Danger High Blood pressure etc and the output classifier membership function are Y0, Y1, Y2. Yn for risk factor values such as Low, High and Normal. The proposed ANFIS system is validated with blood pressure data set values using Mat Lab Fuzzy Tool Box, and simulated output analyse the risk factor value of a human being.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Risk factor analysis of patient based on adaptive neuro fuzzy interface system\",\"authors\":\"M. Mayilvaganan, K. Rajeswari\",\"doi\":\"10.1109/ICCIC.2014.7238348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proposed methodology involved in this paper, is to diagnosis and analysis the health risk factor which is related to Blood Pressure, Pulse rate and Kidney function by Glomerular Filtration Rate (GFR). The computing techniques can handle two most predominant values such as `True' or `False', `1' or `0', `Black' or `White', but Fuzzy Logic, also handle grey values which occur in between `Black' and `White'. The system consists of 234 combination input fields and one output field. This work focus about Adaptive Neuro Fuzzy Interface System (ANFIS) depends on fuzzy logic controller to diagnose the various level of health risk factor value which is aggregated with Blood Pressure, Pulse Rate and Kidney function based on various Input Parameters. In this paper, Fuzzy Logic circuit was developed with 2's Complement in full adder using the input such as Blood Pressure value taken from Systolic and Diastolic value, Pulse Rate and GFR value. Using the OR gate value, Pulse rate and Blood pressure value are compared with Kidney function and getting the output as risk factor value in efficient manner. The input rule based classifier membership functions are X0, X1, X2. Xn for blood pressure values such as Low, Normal, Very Low, Extreme Low Meds, Very Danger Low, Danger too Low BP, Border Line, Very Danger High Blood pressure etc and the output classifier membership function are Y0, Y1, Y2. Yn for risk factor values such as Low, High and Normal. The proposed ANFIS system is validated with blood pressure data set values using Mat Lab Fuzzy Tool Box, and simulated output analyse the risk factor value of a human being.\",\"PeriodicalId\":187874,\"journal\":{\"name\":\"2014 IEEE International Conference on Computational Intelligence and Computing Research\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Computational Intelligence and Computing Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIC.2014.7238348\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Computational Intelligence and Computing Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIC.2014.7238348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Risk factor analysis of patient based on adaptive neuro fuzzy interface system
The proposed methodology involved in this paper, is to diagnosis and analysis the health risk factor which is related to Blood Pressure, Pulse rate and Kidney function by Glomerular Filtration Rate (GFR). The computing techniques can handle two most predominant values such as `True' or `False', `1' or `0', `Black' or `White', but Fuzzy Logic, also handle grey values which occur in between `Black' and `White'. The system consists of 234 combination input fields and one output field. This work focus about Adaptive Neuro Fuzzy Interface System (ANFIS) depends on fuzzy logic controller to diagnose the various level of health risk factor value which is aggregated with Blood Pressure, Pulse Rate and Kidney function based on various Input Parameters. In this paper, Fuzzy Logic circuit was developed with 2's Complement in full adder using the input such as Blood Pressure value taken from Systolic and Diastolic value, Pulse Rate and GFR value. Using the OR gate value, Pulse rate and Blood pressure value are compared with Kidney function and getting the output as risk factor value in efficient manner. The input rule based classifier membership functions are X0, X1, X2. Xn for blood pressure values such as Low, Normal, Very Low, Extreme Low Meds, Very Danger Low, Danger too Low BP, Border Line, Very Danger High Blood pressure etc and the output classifier membership function are Y0, Y1, Y2. Yn for risk factor values such as Low, High and Normal. The proposed ANFIS system is validated with blood pressure data set values using Mat Lab Fuzzy Tool Box, and simulated output analyse the risk factor value of a human being.