smart system using fuzzy, neural and fpga for early diagnosis of renal disease

Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

NITTTR, Chandigarh EDIT -2015 66

Smart System using Fuzzy, Neural and FPGAfor Early Diagnosis of Renal Disease

Ketan K. Acharya, Prof. R.C. Patel1PG Student, 2Associate Professor

1,2Instrumentation & Control Dept., L.D. College of [email protected]

Abstract- In this work we propose, fuzzy logic,neuralnetwork and FPGAbased solution for early diagnosis ofrenal disease. Proposed system also provides apreliminary remedy in terms of medicine by properindication. Pathophysiological parameters for detectingrenal function abnormalities are identified and based onthese data, next state of the patient is predicted usingNeural Network and the system is designed which canprovide the diagnosis for patient’s state i.e normal,moderate or critical using Fuzzy Logic. When the systemdiagnoses it as critical state, preliminary remedialmedicines are also suggested by the system, which can bevery helpful to patients where patient:doctor ratio is verypoor especially in rural areas of developing countries andalso for domestic use for early diagnosis of the disease.FPGA based implementation is also easy to reconfigureand provides lower time to market.

Keywords–FIS(Fuzzy Inference System),FPGA(FieldProgrammable Gate Array),HDL(Hardware DescriptiveLanguage),GFR(Glomerular Filtration Rate),NN(NeuralNetwork),Renal disease(Kidney Related malfunction)

I. INTRODUCTIONKey trends driving the medical instrumentation marketare aging populations, rising healthcare costsaroundthe globe and the need for access to medical diagnosisand treatment in remote andemerging regions and inour own homes.A medical system, also sometimesreferred to as health caresystem is an organization ofpeople, institutions and resourcesto deliver health careservices to meet the health needs of targetpopulations.Presently, diseases in India have emerged asnumberone killer in both urban and rural areas of thecountry. It will be ofgreater value if the diseases arediagnosed in its early stage. Correctdiagnosis of thedisease in its early stage will decrease the death ratedue to different abnormalities.[3]

As per the prevailing scenario in our country, there isonly 1 doctor per 10000 patients in Indian ruralareas.[2].In such a situation, treating patients becomesso hectic and from patients point of view it becomesvery demanding to cope up with health related issues.Under these circumstances, smart system basedsolution for diagnosis and preliminary cure is a need oftime. Renal diseases i.e kidney related malfunctionsare increasing day by day and ignorance to suchdiseases can cause other complications to human bodyand considering this fact early diagnosis of suchdiseases has become a need. Many doctors havesuggested, and are in fact opting for such devices orsystems in which depending on the present results of

pathological readings, diagnosisof the patients can be doneand it can be helpful to patients in taking some correctivemeasures with utmost and timely care. Furtherdevelopments in this field can be helpful to develop aproduct which can be used for domestic applications justlike easy to use BP monitors and Blood Glucose monitors.

II. DIAGNOSIS OF RENAL DISEASE

The kidney has several functions, including the excretion ofwater, soluble wastes, e.g urea and creatinine and foreignmaterials, e.g drugs. It is responsible for the compositionand volume of circulating fluids with respect to waterandelectrolyte balance and acid/base status. It hasanendocrine function playing a part in the production ofvitamin D and erythropoietin and as part of therenin/angiotensin/aldosterone axis. Measurements of renalfunctions rely on measuring, in various ways the degree towhich 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 wastematerials from the blood and expel them from the body asurine. The kidneys also help control the levels of water andvarious minerals in the body. In addition, they are critical tothe production of:vitamin D, red blood cells, hormones thatregulate blood pressure. If the doctor thinks the kidneysmay not be working properly, patient may need kidneyfunction tests. These are simple blood and urine tests thatcan identify problems with the kidneys.There are variousother parameters and the effects of such parameters arealso interrelated. If there is a certain amount of variation ina particular parameter, then only the need arises to go formedical diagnosis for considering the effect of otherparameters.

List of Pathophysiological Parameters to determine kidneymalfunctioning and its effect on cardiovascular systemare:[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-Thistest estimates how well the kidneys are filtering waste. Therate 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 awarning sign of kidney disease. [5]

III.IDENTIFYING-PATHOPHYSIOLOGICAL


67 NITTTR, Chandigarh EDIT-2015

PARAMETERS FOR DIAGNOSISThe smart system designed, considers the mostimportant fundamental pathophysiological parameterswhich are really important to be considered and are ofthose types which can affect the other parameters too ifnot taken care of in the early stage of the diagnosis.Based on research and consultations with doctorsfollowing six important pathophysiological parametersare considered.[5]. Table-1 shows such parameters andtheir ranges for normal,moderate and critical values.

Diastolic BP is more important for renal criticalcondition.Effect of changes in above parametersdirectly affect the renal functions. Parameters,measured are provided to smart system, which will dothe necessary diagnosis of the patient and will providethe solution as required.

IV. SYSTEM FOR EARLY DIAGNOSIS OFRENAL DISEASE AND PRELIMINARY

REMEDYHere, an approach is to design a system in which,based on the pathophysiological parameters of thepatient, criticality of the patient on a particular scalecan be determined.First of all the data for variouspatients are collected from laboratories and hospitals.Then a database is prepared for various patients.Fuzzybased system is used for preparing a complete rulebase for deciding the state of the patient. Based on thevarious ranges of the pathophysiological parameters,the state of the patient can be decided using fuzzylogic.

Smart agent or smart system is prepared based on thedata collected from the laboratories considering thepatients’ profiles. Depending on the types ofpathophysiological parameters, rule base is prepared inMATLAB-SIMULINK.[6].Rule base preparation andmapping using neural network is like an inferenceengine, which helps in preparing an expert system forthe diagnosis purpose. Rule bases are of courseprepared as per the suggestions of doctors and alsoconsidering the research work done in the area ofmedical science.Figure shows the overview ofpreparing asmart system or a smart agent.

It is not possible to accurately diagnose the critical state ofthe patient based on the single data set. Therefore data forvarious pathophysiological parameters are collected fromhospitals and pathology laboratories at regular intervals of10 days or one week (i.e one cycles of data collection) andthen that set of data is used for diagnosing thecritical stateof the patient. 5 cycles of data collection is implementedand then next cycles can be predicted using NeuralNetwork. For neural network based prediction system,nntool of MATLAB is used, where input file is actual dataand based on the next state cycle of data collection targetfile is created for use in nntool. Using input file and targetfile output file is generated which can be seen usingtraining a network and output file from workspace which isconsidered as predicted output stage of a patient.Forexample a sample of a patient with following data isconsidered as input for neural network. e.g for a patientactual data are as follows.

Parameters

Week-1

Week-2

Week-3

Week-4

Week-5

D.BP 75 80 76 81 77

ALB 2.6 2.9 3.1 2 1,9

B.Glucose 230 260 280 267 280

Creatinine 1.36 1.21 1.11 1.15 1.2CR-Protein 70.4 60 65 75 78Potessium-K+ 3.08 3 2.8 2.9 2.6

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

Sr.No

Parameter Normal Moderate

Critical

1 B.P 80-120 90-150 <80 or>120DBP

2 Albumin <+1 +1 >=23 Blood

Glucose<150 150-250 >250

4 Creatinine <1.2 1.2-2 >25 C-R

Protein<6 6-20 >20

6 Potessium <3 3-6 >6

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

Fig: 2Neural network for data prediction

Table: 2 Actual Pathophysiological data of a patient.[8]

Fig: 1 Smart System for proposed Work[2]



In neural network nntool input files and target files areprovided to the network and network outputs will givethe predicted values for the next weeks i.e from week 6to week-10. This will provide the predicted outputwhich is used for early diagnosis and is used for fuzzylogic where specific range of parameter will be usedfor diagnosis purpose.Following table shows thepredicted values of various pathophysiologicalparameters.

Parameters

Week-6

Week-7

Week-8

Week-9

D.BP76.1256

79.0987

77.6789

81.2318

ALB 2.8912 2.6789 3.987 26789B.Glucose

231.3457

270.8789

290.1239

270.3458

Creatinine 1.3214 1.3414 1.3551 1.3510CR-Protein

72.0123

62.3456

66.7612

76.1289

Potessium 3.0908 4.1245 4.1987 3.1289

Predicted data can be provided directly or throughtelemedicine to the Fuzzy logic part of the proposedsmart system.Predicted data are provide tofuzzyinference system and as per the range ofpathophysiological parameters, fuzzy rule bases areformed. MATLAB-SIMULINK tool is used todetermine the intervals and rule bases and as per thoseprepared rule bases, patient’s state can be determinedas normal, moderate or critical.As shown in figure-4,membership functions are defined for all importantsix parameters for diagnosis of renal critical condition.The shapes of membership functions are determinedbased on the range of variation of the values. As shownin table-1, identified six parameters are having thevariation in specified ranges which are used todetermine the shapes of specific membershipfunctions. Generally used membership functions areGaussian, triangular and trapezoidal. Depending on thevariation of the values and also based on the expectedoutcome following types of shapes are considered forvarious membership functions.1.Blood Pressre-Trapezoidal type, 2- Albumin- Gaussian type3-Bloodglucose –Gaussian type, 4-Creatinine- Gaussian type

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

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

IF ( Blood Pressure is moderate AND Albumin is moderateAND Blood glucose is moderate AND Potessium ismoderate C-Rprotein is critical AND Creatinine ismoderateAND Potessium is moderate) THEN patient’s state ismoderate. Similarly other rules for normal, moderate andcritical state diagnosis purpose are determined.Based onthese rule base fuzzy controller is prepared which is used asan integral part of the model prepared using simulink asshown below which makes a diagnosis for the critical stateof a patient and also suggests preliminary medicine forimmediate treatment. Criticality on scale of 1 to 5 is alsodisplayed by the model.

Model prepared in simulink is as follows.

Table: 3 Predicted Pathophysiological data of a patient

Fig: 4 Shapes of membership functions

Fig: 5 Rule bases for membership functions

Fig: 3Regression plot for predicted data




Parameters

Week-6

Week-7

Week-8

Week-9

D.BP76.1256

79.0987

77.6789

81.2318

ALB 2.8912 2.6789 3.987 26789B.Glucose

231.3457

270.8789

290.1239

270.3458


72.0123

62.3456

66.7612

76.1289

Potessium 3.0908 4.1245 4.1987 3.1289













Parameters

Week-6

Week-7

Week-8

Week-9

D.BP76.1256

79.0987

77.6789

81.2318

ALB 2.8912 2.6789 3.987 26789B.Glucose

231.3457

270.8789

290.1239

270.3458


72.0123

62.3456

66.7612

76.1289

Potessium 3.0908 4.1245 4.1987 3.1289












Proposed smart system is modeled using Simulinkwhere the patient’s data are entered based on thepredicted output of neural netwoerk. If patient’s state iscritical then system also suggests the medicine1[9] i.e-ANGIOTENSIN—ACEIS and if it is moderate then itsuggests medicine2 ANGIOTENSIN-II RECEPTORas 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 THESYSTEM.

Based on this simulation, the system is implementedusing FPGA. FPGAs are chosen for implementationconsidering the following reason: 1.They can beapplied to a wide range of logic gates starting with tensof thousands up to few millions gates.They can bereconfigured to change logic function while resident inthe system.FPGAs have short design cycle that leadsto fairly inexpensive logic design.FPGAs haveparallelism in their nature. Thus, they have parallelcomputing environment and allows logic cycle designto work parallel.They have powerful design,programming and syntheses tools.FPGAs are havinglower time to market, lower cost and reconfigurablecharacteristics which makes it a choice for preferredhardware. Here preferred system is XilinxSpartan3XC3S1000-4FG456, which is programmedusing Altium NB1 and Evaluation board ofXilinx.System is designed using Xilinx ISE and ishaving following input and output parameters.

Inputs OutputsBlood Pressure-Diastolic-dbp

opbp

Blood Glucose-bg opbgAlbumin-alb opalbCreatinine-crt opcrtC-RProteiin-crp opcrpPotessium-K+ opk

Note- Medicine asprescribed by a doctor is alsosuggested when patient’sstate is critical/moderate.i.emedicine-1 or 2.

Patient’s Statein outputpstatec-Criticalpstatem-Moderatepstaten-Normal

VI. RESULT ANALYSISFor purpose of this work, data has been collected forvarious patients from laboratory and hospital. Data of 40patients at the interval of 10 days or one week (cycle) arecollected. Total 5 cycles of such data collection isperformed. Total 200 data are tested using Bayesianmethod for accuracy of the system.[2].Testing this system using Bayesian method,Let a= Numberof patients where diagnostic test gives positive result andpatient really has a diasese,b= Number of patients wherethe diagnostic test gives a positive result and patient doesnot have disease, c = number of patients where diagnostictest gives negative result and patient really has disease andd=number of patients where diagnostic test yields anegative result and patient does not have disease.In thiscase 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.CONCLUSIONProposed system is used to predict the nextpathophysiological state of the patient using neural networkand then to diagnose the renal disease based on fuzzy logic.Over all system is implemented using FPGA. The systemgives an accuracy of 90 %, which is tested using Bayesianmethod and also validated using actual patients’ data fromhospital. The system really becomes helpful for the patientsas well as doctors for early diagnosis of the renal diseasesand it also suggests a preliminary remedy for earlytreatment of patient where there is really a need ofsystems.System also gives the criticality on the scale of 5starting from 1 to 5 and further medications as well as testscan be suggested. Futher efforts can be made to improveaccuracy, providing user defined parameters andtelemedicine based approach.

REFERENCES

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REFERENCES

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[1] R. R. Janghel-IIIT GwaliorDecision Support System for FetalDelivery usingSoft Computing Techniques.pp-1-2 IEEE 2009[2] .Development of an FPGA based fuzzy neural network systemfor early diagnosis of critical health condition of a patient:ShubhajitRoy chowdhary, Hiranmay Saha Computers in Biology andMedicine-pp-1-3 pp-4-5 Elsevier l vol:40-2010[3] Cancer Diagnosis using modified fuzzy Neural Network-Universal Journal of Computer Science and Engineering Technology1 (2), pp 73-78, Nov. 2010.[4] Cheng-Jian Lin, Chi-Yung Lee Implementation of a neuro-fuzzynetwork with on-chip learning and its applications pp-1 ELSEVIER-2010[5]Crystin C,Kidney functions and cardiovascular risk factors inprimary hypertension: Jaoa Victor Salvado,Ana Karina Franca-Kidney Disease Prevention –pp-1-4 Elsevier Journal vol-59-2012.[6]Applications of neuro fuzzy systems: A brief review and futureoutline Samarjit Kara, Sujit Dasb, Pijush Ghosh pp 3-5 ELSEVIERjournal-2013.[7]Applications of Neuro Fuzzy systems: A brief review and futureoutline. Samarjit Kara, Sujit Das, Pijush Kanti Ghosh Applied Softpp 2-5 Computing-Elsevier Journal vol-15-2013.[8]Sheefa Hospital,, Khevana Patho.Lab, Satyam hospital-Ahmedaba, 2014[9] Medicines for early stage Chronic Disease- A review of researchfor adults with Kidney Disease.pp1-12 2014.

smart system using fuzzy, neural and fpga for early diagnosis of renal disease

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