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 patient’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.

1. 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 FPGA
for Early Diagnosis of Renal Disease
Ketan K. Acharya, Prof. R.C. Patel
1
PG Student, 2
Associate Professor
1,2
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 patient’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.
Keywords–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
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

2. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
67 NITTTR, Chandigarh EDIT-2015
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.
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
patients’ 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.
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.
Paramete
rs
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 1.2
CR-
Protein 70.4 60 65 75 78
Potessium
-K+ 3.08 3 2.8 2.9 2.6
Now using Neural Network the prediction for next state of
data is predicted. [7]
Sr.N
o
Parameter Normal Moderat
e
Critica
l
1 B.P 80-120 90-150 <80 or
>120
DBP
2 Albumin <+1 +1 >=2
3 Blood
Glucose
<150 150-250 >250
4 Creatinine <1.2 1.2-2 >2
5 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]

3. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 68
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.
Paramete
rs
Week-
6
Week-
7
Week-
8
Week-
9
D.BP
76.125
6
79.098
7
77.678
9
81.231
8
ALB 2.8912 2.6789 3.987 26789
B.Glucos
e
231.34
57
270.87
89
290.12
39
270.34
58
Creatinin
e 1.3214 1.3414 1.3551 1.3510
CR-
Protein
72.012
3
62.345
6
66.761
2
76.128
9
Potessium 3.0908 4.1245 4.1987 3.1289
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 figure-
4,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 Pressre-
Trapezoidal type, 2- Albumin- Gaussian type3-Blood
glucose –Gaussian type, 4-Creatinine- Gaussian type
5- Protein – Trapezoidal,6 – Potessium- Trapezoidal 7-
Output 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].
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.
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.
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
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 68
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.
Paramete
rs
Week-
6
Week-
7
Week-
8
Week-
9
D.BP
76.125
6
79.098
7
77.678
9
81.231
8
ALB 2.8912 2.6789 3.987 26789
B.Glucos
e
231.34
57
270.87
89
290.12
39
270.34
58
Creatinin
e 1.3214 1.3414 1.3551 1.3510
CR-
Protein
72.012
3
62.345
6
66.761
2
76.128
9
Potessium 3.0908 4.1245 4.1987 3.1289
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 figure-
4,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 Pressre-
Trapezoidal type, 2- Albumin- Gaussian type3-Blood
glucose –Gaussian type, 4-Creatinine- Gaussian type
5- Protein – Trapezoidal,6 – Potessium- Trapezoidal 7-
Output 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].
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.
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.
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
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 68
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.
Paramete
rs
Week-
6
Week-
7
Week-
8
Week-
9
D.BP
76.125
6
79.098
7
77.678
9
81.231
8
ALB 2.8912 2.6789 3.987 26789
B.Glucos
e
231.34
57
270.87
89
290.12
39
270.34
58
Creatinin
e 1.3214 1.3414 1.3551 1.3510
CR-
Protein
72.012
3
62.345
6
66.761
2
76.128
9
Potessium 3.0908 4.1245 4.1987 3.1289
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 figure-
4,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 Pressre-
Trapezoidal type, 2- Albumin- Gaussian type3-Blood
glucose –Gaussian type, 4-Creatinine- Gaussian type
5- Protein – Trapezoidal,6 – Potessium- Trapezoidal 7-
Output 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].
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.
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.
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

4. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
69 NITTTR, Chandigarh EDIT-2015
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.e-
ANGIOTENSIN—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 Outputs
Blood Pressure-Diastolic-
dbp
opbp
Blood Glucose-bg opbg
Albumin-alb opalb
Creatinine-crt opcrt
C-RProteiin-crp opcrp
Potessium-K+ opk
Note- Medicine as
prescribed by a doctor is also
suggested when patient’s
state is critical/moderate.i.e
medicine-1 or 2.
Patient’s State
in output
pstatec-
Critical
pstatem-
Moderate
pstaten-
Normal
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.
REFERENCES
1
Out1
simout
To Workspace
Scope
Repeating
Sequence6
Repeating
Sequence5
Repeating
Sequence4
Repeating
Sequence3
Repeating
Sequence2
Repeating
Sequence1
0
NORMAL
1
MODERATE
1
MEDICINE-2
0
MEDICINE-1
Interval Test8
Interval Test6
Interval Test5
Interval Test4
Interval Test3
Interval Test2
Interval Test1
Interval Test
FuzzyLogic
Controller
with Ruleviewer 0.5
Display
0
CRITICAL-5
0
CRITICAL-4
0
CRITICAL-3
0
CRITICAL-2
0
CRITICAL-1
0
CRITICAL
Fig: 6 System modeling using Simulink
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
69 NITTTR, Chandigarh EDIT-2015
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.e-
ANGIOTENSIN—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 Outputs
Blood Pressure-Diastolic-
dbp
opbp
Blood Glucose-bg opbg
Albumin-alb opalb
Creatinine-crt opcrt
C-RProteiin-crp opcrp
Potessium-K+ opk
Note- Medicine as
prescribed by a doctor is also
suggested when patient’s
state is critical/moderate.i.e
medicine-1 or 2.
Patient’s State
in output
pstatec-
Critical
pstatem-
Moderate
pstaten-
Normal
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.
REFERENCES
1
Out1
simout
To Workspace
Scope
Repeating
Sequence6
Repeating
Sequence5
Repeating
Sequence4
Repeating
Sequence3
Repeating
Sequence2
Repeating
Sequence1
0
NORMAL
1
MODERATE
1
MEDICINE-2
0
MEDICINE-1
Interval Test8
Interval Test6
Interval Test5
Interval Test4
Interval Test3
Interval Test2
Interval Test1
Interval Test
FuzzyLogic
Controller
with Ruleviewer 0.5
Display
0
CRITICAL-5
0
CRITICAL-4
0
CRITICAL-3
0
CRITICAL-2
0
CRITICAL-1
0
CRITICAL
Fig: 6 System modeling using Simulink
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
69 NITTTR, Chandigarh EDIT-2015
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.e-
ANGIOTENSIN—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 Outputs
Blood Pressure-Diastolic-
dbp
opbp
Blood Glucose-bg opbg
Albumin-alb opalb
Creatinine-crt opcrt
C-RProteiin-crp opcrp
Potessium-K+ opk
Note- Medicine as
prescribed by a doctor is also
suggested when patient’s
state is critical/moderate.i.e
medicine-1 or 2.
Patient’s State
in output
pstatec-
Critical
pstatem-
Moderate
pstaten-
Normal
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.
REFERENCES
Fig: 6 System modeling using Simulink

5. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 70
[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 Network-
Universal 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 ELSEVIER-
2010
[5]Crystin C,Kidney functions and cardiovascular risk factors in
primary 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 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 hospital-
Ahmedaba, 2014
[9] Medicines for early stage Chronic Disease- A review of research
for adults with Kidney Disease.pp1-12 2014.