Smart System using Fuzzy, Neural and FPGA for Early Diagnosis of Renal Disease by IJEEE (Elixir Publications)

Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Smart System using Fuzzy, Neural and FPGA for Early Diagnosis of Renal Disease Ketan K. Acharya, Prof. R.C. Patel 1

1,2

PG Student, 2Associate Professor Instrumentation & Control Dept., L.D. College of Engineering-Ahmedabad. 1

ketankacharya@gmail.com

Abstract- In this work we propose, fuzzy logic,neural network and FPGAbased solution for early diagnosis of renal disease. Proposed system also provides a preliminary remedy in terms of medicine by proper indication. Pathophysiological parameters for detecting renal function abnormalities are identified and based on these data, next state of the patient is predicted using Neural Network and the system is designed which can provide the diagnosis for patientâ&#x20AC;&#x2122;s state i.e normal, moderate or critical using Fuzzy Logic. When the system diagnoses it as critical state, preliminary remedial medicines are also suggested by the system, which can be very helpful to patients where patient:doctor ratio is very poor especially in rural areas of developing countries and also for domestic use for early diagnosis of the disease. FPGA based implementation is also easy to reconfigure and provides lower time to market. Keywordsâ&#x20AC;&#x201C;FIS(Fuzzy Inference System),FPGA(Field Programmable Gate Array),HDL(Hardware Descriptive Language),GFR(Glomerular Filtration Rate),NN(Neural Network),Renal disease(Kidney Related malfunction)

I. INTRODUCTION Key trends driving the medical instrumentation market are aging populations, rising healthcare costsaround the globe and the need for access to medical diagnosis and treatment in remote andemerging regions and in our own homes.A medical system, also sometimes referred to as health caresystem is an organization of people, institutions and resourcesto deliver health care services to meet the health needs of targetpopulations. Presently, diseases in India have emerged as numberone killer in both urban and rural areas of the country. It will be ofgreater value if the diseases are diagnosed in its early stage. Correctdiagnosis of the disease in its early stage will decrease the death rate due to different abnormalities.[3] As per the prevailing scenario in our country, there is only 1 doctor per 10000 patients in Indian rural areas.[2].In such a situation, treating patients becomes so hectic and from patients point of view it becomes very demanding to cope up with health related issues. Under these circumstances, smart system based solution for diagnosis and preliminary cure is a need of time. Renal diseases i.e kidney related malfunctions are increasing day by day and ignorance to such diseases can cause other complications to human body and considering this fact early diagnosis of such diseases has become a need. Many doctors have suggested, and are in fact opting for such devices or systems in which depending on the present results of NITTTR, Chandigarh

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pathological readings, diagnosisof the patients can be done and it can be helpful to patients in taking some corrective measures with utmost and timely care. Further developments in this field can be helpful to develop a product which can be used for domestic applications just like easy to use BP monitors and Blood Glucose monitors. II. DIAGNOSIS OF RENAL DISEASE The kidney has several functions, including the excretion of water, soluble wastes, e.g urea and creatinine and foreign materials, e.g drugs. It is responsible for the composition and volume of circulating fluids with respect to water andelectrolyte balance and acid/base status. It has anendocrine function playing a part in the production of vitamin D and erythropoietin and as part of the renin/angiotensin/aldosterone axis. Measurements of renal functions rely on measuring, in various ways the degree to which the kidney is successful in these roles. The kidneys play several vital roles in maintaining health. [3].One of their most important jobs is to filter waste materials from the blood and expel them from the body as urine. The kidneys also help control the levels of water and various minerals in the body. In addition, they are critical to the production of:vitamin D, red blood cells, hormones that regulate blood pressure. If the doctor thinks the kidneys may not be working properly, patient may need kidney function tests. These are simple blood and urine tests that can identify problems with the kidneys.There are various other parameters and the effects of such parameters are also interrelated. If there is a certain amount of variation in a particular parameter, then only the need arises to go for medical diagnosis for considering the effect of other parameters. List of Pathophysiological Parameters to determine kidney malfunctioning and its effect on cardiovascular system are:[5],[8],body mass index(BMI), blood pressure, glomerular filteration rate (GFR), albumin, micro albumin, blood glucose, cholesterol, blood urea, serum creatinine, serum crystine, haemoglobin, c-reactive protein, creatinine, potessium- K+.GFR i.e Glomerular Filtration Rate test-This test estimates how well the kidneys are filtering waste. The rate is calculated by taking several factors into account, such astest results, specifically creatinine levels,age, gender, race, height,weight, Any result lower than 60 is a warning sign of kidney disease. [5]

III.IDENTIFYING-PATHOPHYSIOLOGICAL 66

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Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Table: 1 Range of identified pathophysiological parameters.[5]

Sr.N o 1

Parameter

Normal

B.P

80-120

Moderat e 90-150

2 3

Albumin Blood Glucose Creatinine C-R Protein Potessium

<+1 <150

+1 150-250

Critica l <80 or >120 DBP >=2 >250

<1.2 <6

1.2-2 6-20

>2 >20

4 5 6

Fig: 1 Smart System for proposed Work[2]

3-6

PARAMETERS FOR DIAGNOSIS The smart system designed, considers the most important fundamental pathophysiological parameters which are really important to be considered and are of those types which can affect the other parameters too if not taken care of in the early stage of the diagnosis. Based on research and consultations with doctors following six important pathophysiological parameters are considered.[5]. Table-1 shows such parameters and their ranges for normal,moderate and critical values. Diastolic BP is more important for renal critical condition.Effect of changes in above parameters directly affect the renal functions. Parameters, measured are provided to smart system, which will do the necessary diagnosis of the patient and will provide the solution as required.

It is not possible to accurately diagnose the critical state of the patient based on the single data set. Therefore data for various pathophysiological parameters are collected from hospitals and pathology laboratories at regular intervals of 10 days or one week (i.e one cycles of data collection) and then that set of data is used for diagnosing thecritical state of the patient. 5 cycles of data collection is implemented and then next cycles can be predicted using Neural Network. For neural network based prediction system, nntool of MATLAB is used, where input file is actual data and based on the next state cycle of data collection target file is created for use in nntool. Using input file and target file output file is generated which can be seen using training a network and output file from workspace which is considered as predicted output stage of a patient.For example a sample of a patient with following data is considered as input for neural network. e.g for a patient actual data are as follows. Table: 2 Actual Pathophysiological data of a patient.[8]

IV. SYSTEM FOR EARLY DIAGNOSIS OF RENAL DISEASE AND PRELIMINARY REMEDY Here, an approach is to design a system in which, based on the pathophysiological parameters of the patient, criticality of the patient on a particular scale can be determined.First of all the data for various patients are collected from laboratories and hospitals. Then a database is prepared for various patients.Fuzzy based system is used for preparing a complete rule base for deciding the state of the patient. Based on the various ranges of the pathophysiological parameters, the state of the patient can be decided using fuzzy logic. Smart agent or smart system is prepared based on the data collected from the laboratories considering the patientsâ&#x20AC;&#x2122; profiles. Depending on the types of pathophysiological parameters, rule base is prepared in MATLAB-SIMULINK.[6].Rule base preparation and mapping using neural network is like an inference engine, which helps in preparing an expert system for the diagnosis purpose. Rule bases are of course prepared as per the suggestions of doctors and also considering the research work done in the area of medical science.Figure shows the overview of preparing asmart system or a smart agent.

Paramete rs D.BP ALB B.Glucose Creatinine CRProtein Potessium -K+

Week -1 75 2.6 230 1.36

Week -2 80 2.9 260 1.21

Week -3 76 3.1 280 1.11

Week -4 81 2 267 1.15

Week -5 77 1,9 280 1.2

70.4

3.08

2.8

2.9

2.6

Now using Neural Network the prediction for next state of data is predicted. [7]

Fig: 2Neural network for data prediction

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Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

5- Protein – Trapezoidal,6 – Potessium- Trapezoidal 7Output Patient Stage- Trapezoidal.FIS editor in MATLAB is used for preparing the membership functions having such shapes and accordingly the output i.e patient’s state is also selected which indicates the diagnosed state of the patient based on the membership function values.[ 01].

Fig: 3Regression plot for predicted data

In neural network nntool input files and target files are provided to the network and network outputs will give the predicted values for the next weeks i.e from week 6 to week-10. This will provide the predicted output which is used for early diagnosis and is used for fuzzy logic where specific range of parameter will be used for diagnosis purpose.Following table shows the predicted values of various pathophysiological parameters. Table: 3 Predicted Pathophysiological data of a patient

Paramete rs

Week6 76.125 6

Week7 79.098 7

Week8 77.678 9

Week9 81.231 8

ALB B.Glucos e Creatinin e CRProtein

2.8912 231.34 57

2.6789 270.87 89

3.987 290.12 39

26789 270.34 58

1.3214 72.012 3

1.3414 62.345 6

1.3551 66.761 2

1.3510 76.128 9

Potessium

3.0908

4.1245

4.1987

3.1289

D.BP

Predicted data can be provided directly or through telemedicine to the Fuzzy logic part of the proposed smart system.Predicted data are provide tofuzzy inference system and as per the range of pathophysiological parameters, fuzzy rule bases are formed. MATLAB-SIMULINK tool is used to determine the intervals and rule bases and as per those prepared rule bases, patient’s state can be determined as normal, moderate or critical.As shown in figure4,membership functions are defined for all important six parameters for diagnosis of renal critical condition. The shapes of membership functions are determined based on the range of variation of the values. As shown in table-1, identified six parameters are having the variation in specified ranges which are used to determine the shapes of specific membership functions. Generally used membership functions are Gaussian, triangular and trapezoidal. Depending on the variation of the values and also based on the expected outcome following types of shapes are considered for various membership functions.1.Blood PressreTrapezoidal type, 2- Albumin- Gaussian type3-Blood glucose –Gaussian type, 4-Creatinine- Gaussian type NITTTR, Chandigarh

Fig: 4 Shapes of membership functions

After deciding the membership functions for 6 parameters, total 64 rules i.e 2^6 rules are formed to determine the normal, moderate or critical state of the patient.Rules are prepared as per the combinations of the effects of various pathophysiological parameters. These rules finallyy determine the stae of the patient, i.e Normal, Moderate or Critical.e.g IF (Blood Pressure is Critical AND Albumin is normal AND Blood Glucose is critial AND Potessium is critical and AND C-R Protein is critical AND Creatinine is critical AND Potessium is critical) THEN patient’s state is critical.

Fig: 5 Rule bases for membership functions

IF ( Blood Pressure is moderate AND Albumin is moderate AND Blood glucose is moderate AND Potessium is moderate C-Rprotein is critical AND Creatinine is moderate AND Potessium is moderate) THEN patient’s state is moderate. Similarly other rules for normal, moderate and critical state diagnosis purpose are determined.Based on these rule base fuzzy controller is prepared which is used as an integral part of the model prepared using simulink as shown below which makes a diagnosis for the critical state of a patient and also suggests preliminary medicine for immediate treatment. Criticality on scale of 1 to 5 is also displayed by the model.

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Model prepared in simulink is as follows.

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simout

Repeating Sequence6

Interval Test5

To Workspace

Vol. 2, Spl. Issue 1 (2015)

NORMAL

MODERATE

CRITICAL

NoteMedicine as prescribed by a doctor is also suggested when patient’s state is critical/moderate.i.e medicine-1 or 2.

Out1 0

Repeating Sequence1

Repeating Sequence2

Interval Test6 Fuzzy Logic Controller with Ruleviewer

MEDICINE-1

Scope 0.5

Patient’s State in output pstatecCritical pstatemModerate pstatenNormal

Display Repeating Sequence4

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MEDICINE-2

Interval Test8 0

CRITICAL-1 Repeating Sequence3

Interval Test 0 Interval Test1

CRITICAL-2

Interval Test2

CRITICAL-3

Interval Test3

CRITICAL-4

Interval Test4

CRITICAL-5

Repeating Sequence5

Fig: 6 System modeling using Simulink

Proposed smart system is modeled using Simulink where the patient’s data are entered based on the predicted output of neural netwoerk. If patient’s state is critical then system also suggests the medicine1[9] i.eANGIOTENSIN—ACEIS and if it is moderate then it suggests medicine2 ANGIOTENSIN-II RECEPTOR as preliminary treatment in case of emergencies. Further advanced tests are also suggested if required. Medicines can be changed as per doctor’s advice. V. FPGA IMPLEMENTATION FOR THE SYSTEM. Based on this simulation, the system is implemented using FPGA. FPGAs are chosen for implementation considering the following reason: 1.They can be applied to a wide range of logic gates starting with tens of thousands up to few millions gates.They can be reconfigured to change logic function while resident in the system.FPGAs have short design cycle that leads to fairly inexpensive logic design.FPGAs have parallelism in their nature. Thus, they have parallel computing environment and allows logic cycle design to work parallel.They have powerful design, programming and syntheses tools.FPGAs are having lower time to market, lower cost and reconfigurable characteristics which makes it a choice for preferred hardware. Here preferred system is Xilinx Spartan3XC3S1000-4FG456, which is programmed using Altium NB1 and Evaluation board of Xilinx.System is designed using Xilinx ISE and is having following input and output parameters. Inputs Blood Pressure-Diastolicdbp Blood Glucose-bg Albumin-alb Creatinine-crt C-RProteiin-crp Potessium-K+ 69

Outputs opbp opbg opalb opcrt opcrp opk

VI. RESULT ANALYSIS For purpose of this work, data has been collected for various patients from laboratory and hospital. Data of 40 patients at the interval of 10 days or one week (cycle) are collected. Total 5 cycles of such data collection is performed. Total 200 data are tested using Bayesian method for accuracy of the system.[2]. Testing this system using Bayesian method,Let a= Number of patients where diagnostic test gives positive result and patient really has a diasese,b= Number of patients where the diagnostic test gives a positive result and patient does not have disease, c = number of patients where diagnostic test gives negative result and patient really has disease and d=number of patients where diagnostic test yields a negative result and patient does not have disease.In this case a =29, b=4, c=3, d=4.Total a+b+c+d=40. ( ) Therefore prevalence of diagnosis = = =0.8 And Sensitivity of diagnosis =

(

= =0.9

)

Thus the proposed smart system gives an accuracy of 90%. VII.CONCLUSION Proposed system is used to predict the next pathophysiological state of the patient using neural network and then to diagnose the renal disease based on fuzzy logic. Over all system is implemented using FPGA. The system gives an accuracy of 90 %, which is tested using Bayesian method and also validated using actual patients’ data from hospital. The system really becomes helpful for the patients as well as doctors for early diagnosis of the renal diseases and it also suggests a preliminary remedy for early treatment of patient where there is really a need of systems.System also gives the criticality on the scale of 5 starting from 1 to 5 and further medications as well as tests can be suggested. Futher efforts can be made to improve accuracy, providing user defined parameters and telemedicine based approach.

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REFERENCES EDIT-2015

Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

[1] R. R. Janghel-IIIT GwaliorDecision Support System for Fetal Delivery usingSoft Computing Techniques.pp-1-2 IEEE 2009 [2] .Development of an FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient:Shubhajit Roy chowdhary, Hiranmay Saha Computers in Biology and Medicine-pp-1-3 pp-4-5 Elsevier l vol:40-2010 [3] Cancer Diagnosis using modified fuzzy Neural NetworkUniversal Journal of Computer Science and Engineering Technology 1 (2), pp 73-78, Nov. 2010. [4] Cheng-Jian Lin, Chi-Yung Lee Implementation of a neuro-fuzzy network with on-chip learning and its applications pp-1 ELSEVIER2010 [5]Crystin C,Kidney functions and cardiovascular risk factors in primary hypertension: Jaoa Victor Salvado,Ana Karina FrancaKidney Disease Prevention â&#x20AC;&#x201C;pp-1-4 Elsevier Journal vol-59-2012. [6]Applications of neuro fuzzy systems: A brief review and future outline Samarjit Kara, Sujit Dasb, Pijush Ghosh pp 3-5 ELSEVIER journal-2013. [7]Applications of Neuro Fuzzy systems: A brief review and future outline. Samarjit Kara, Sujit Das, Pijush Kanti Ghosh Applied Soft pp 2-5 Computing-Elsevier Journal vol-15-2013. [8]Sheefa Hospital,, Khevana Patho.Lab, Satyam hospitalAhmedaba, 2014 [9] Medicines for early stage Chronic Disease- A review of research for adults with Kidney Disease.pp1-12 2014.

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