Presented by

Shaikh Abdul Hannan

Under the guidance of

Dr. R.J. Ramteke

North Maharashtra University, Jalgaon.

Development of an Expert System for Diagnosis

and Appropriate Medical Prescription of Heart

Disease using Neural Network Techniques

The term heart disease ◦ to a number of illness

circulatory system,

heart and blood vessels.

Heart attack ◦ sudden pain in chest with sweating,

breathing difficulty, giddiness, palpitations etc.

Heart disease◦ one or more valves

in the heart are

through heart properly .

16

• This can put

– on the heart

• breathlessness and sweating .

• The patients

– under proper treatment

• many useful years.

• Heart disease factor

• in human beings

– medical research area.

17

Symptoms of heart disease

◦ depend on the duration, severity, and type of disease.

Weakness or dizziness

Nausea

Sweating

Vomiting

Pain in the chest or arm

Discomfort to the back, jaw, or throat

Choking feeling (may feel like heartburn)

Shortness of breath

Rapid or irregular heartbeats

18

A complete Medical evaluation ◦ Medical history

◦ Systems enquiry

◦ Physical examination

◦ Appropriate laboratory or imaging studies

◦ Analysis of data

◦ Medical decision making to obtain

diagnoses

treatment plan.

19

The data is collected◦ from Sahara Hospital Jinsi, Roshan Gate, Aurangabad

under supervision of

Dr. Abdul Jabbar (MD Medicine).

All the data is prepared◦ from the patients case papers.

20

Patient Case

paper

This is one of the

patient report from

which the data is

recorded.

21

22

23

24

25

The data is collected as symptoms and◦ information about patients details

Such as Previous History (P1)

Present symptoms or disease (P2)

Personal History (P3)

Physical Examination (P4)

Cardio Vascular System (CVS)

Respiratory System (RS)

Per Abdomen (PA)

Central Nervous system (CNS)

Electro-Cardio-Gram (ECG)

and Blood Investigation (BI)

are recorded.

26

The 300 data collected ◦ regarding heart patients and

are prepared in different Excel Sheets which contains codes of

each symptoms of the patient.

In one excel sheet ◦ 13 sub-sheets has taken

for each field of information

such as for Previous History one sub-sheet has taken and given the name is given (P1),

for Present disease the second sub-sheet and the name is given (P2),

for Personal History the third sheet is taken and the name is given (P3),

like this 13 different sheet has taken for different fields .

27

Code Name of Disease Code Present Disease

1 Hypertension 1 Chest Pain / Discomfort

2 Diabetes Mellitus 2 Retrosternal Pain

3 TB 3 Palpitations

4 Bronchial Asthama 4 Breathlessness

5 Hyperthyroidism 5 Sweating

6 Hypothyrodism 6 Perspiration

7 Old Ischaemic heart disease 7 Giddiness

8 Nil 8 Nausea / Vomiting

9 Interstitial Lung disease (ILD) 9 Epigastric Pain

10 Cerebrovascular Accident(CVA) 10 Left Arm pain

11 Rheumatoid arthritis 11 Syncope

12 Haemorrhoids(Bleeding piles) 12 Unconciousness

13 Rheumatic heart disease 13 Uneasiness/restlessness

14 Atrial fibrillation 14 Back pain

15 Chronic Obstructive Pulmonary Disease 15 Heavyness in Chest

16 Supra Ventricular Tachycardia (S.V.T.) 16 Difficulty in breathing

17 Fibroid with Menorrhagia 17 Cough

18 Dilated Cardiomyopathy 18 Swelling over feet

19 convulsion (fits)

20 Headache

21 Heartburn

22 Jaw / Throat Pain

23 Decrease Apetite

24 Epistaxis (Bleeding from Nose)

Code Personal History 25 Haemoptysis

1 Smoking 26 Both Shoulder Pain

2 Tobacco 27 Bleeding per rectum (PR)

3 Alcohol 28 Haemetemesis (Blood Wrmtlne)

4 Nil 29 Loose motion

Previous History (P1) Present Disease (P2)

Personnel History (P3)

28

Patient Disease Information

Code Physical Examination Code Symptoms

1 altered Conciousness 1 Heart Sounds

2 Orientation 2 Normal heart rate

3 Dyspnoea 3 Tachycardia

4 Fever 4 Bradycardia

5 Low Pulse rate 5 Regular Heart Rhythm

6 Normal Pulse rate 6 Irregular Heart Rhythm

7 High Pulse rate 7 Gallop sound

8 Low systolic Blood Pressure 8 No Abnormality Detected (NAD)

9 Normal Blood Pressure

10 High Blood Pressure

11 Low Respiratory rate Code Findings

12 Normal Respiratory rate 1 Breath Sounds preserved

13 High Respiratory rate 2 Breath Sounds reduced

14 Pallor 3 Basal crepts

15 Jaundice 4 No Abnormality Detected (NAD)

16 Oedema Feet 5 Ronchi

17 Perspiration

18 Saturation Less than 90%

19 NAD Code Finding

20 restlessness 1 Liver (hepatomegaly)

21 cyanosis 2 Spleen (Splenomegaly)

22 Irregular pulse 3 Free Fluid Present

23 General Condition - Not satisfactory 4 Abdominal Distension

24 Facial Puffiness 5 No Abnormality Detected (NAD)

25 cold extremeties 6 Obesity

Respiratory System (RS)

Per Abdomen (PA)

Physical Examination (P4) Cardio-Vascular-System

Patient Disease Information

29

Patient Disease Information

Code Finding Code Findings

1 Conciousness 1 Cardiac Enzymes (High)

2 Orientation 2 Blood Sugar Test Normal

3 Focal Deficit 3 Blood Sugar Test Low

4 No Abnormality Detected (NAD) 4 Blood Sugar Test High

5 Restlessness 5 Kidney Function Test deranged

6 Lipid Profile normal

7 Lipid Profile Abnormal

Code Finding 8 Complete Blood Count Normal

1 ST Elevation 9 Leucocytosis

2 Anterior Wall 10 Anaemia

3 Anteroseptal 11 Thrombocytopenia

4 Inferior 12 Urine Routine

5 Inferoposterior 13 Troponin T/I ( positive )

6 Lateral 14 No Abnormality Detected (NAD)

7 Septal 15 Chest X ray (Cardiomegaly)

8 High Lateral 16 2D-Echo RWMA

9 T Wave inversion 17 2D-Echo LV CLOT

10 ST Depression 18 2d-echo LVH

11 QS complex 19 Hypokalemia

12 LBBB 20 Hyponatremia

13 inferior & lateral(4&6) 21 Thyroid Profile (Abnormal)

14 atrial fibrillation 22 20-Echo - Poor LVEF

15 RBBB 23 Hypercalemia

16 VPB's 24 Chest X-ray (COPD)

17 Sinus Tachycardia

18 Sinus Rhythm

19 Supraventricular tachycardia S.V.T.

20 Left Ventricular hypertrophy (LVH)

21 Ventricular tachycardia / Fibrillation

Electro-Cardio-Gram (ECG)

Blood Investigation (BI)Central Nervous System (CNS)

30

Patient Medicines prescribed by the doctor

MID Medicine name MID Medicine name

1 Alprazolam 27 Fortwin Inj

2 Amlodepine 28 Diazepam Inj

3 Aspirin 29 Nitroglycerin Inj

4 Atenolol 30 Ciprofloxacin Inj

5 atorvastatin 31 Cardarone

6 Clopidogrel 32 Dobutamine Inj

7 DIGOXIN 33 Levolin (Nebolize)

8 Diltiazem 34 Methyl prednisolone

9 Diphenylhydantoin Sodium 35 Oral Hypoglycaemics

10 Enalapril 36 Lactulose syrup

11 Furosemide 37 Eltroxin

12 Ethamsylate 38 Dextrose 25% Inj

13 Insulin Inj 39 Dopamine

14 Iso sorbide dinitrate 40 Warfarin sodium

15 Losartan 41 Iron Capsule

16 Metoprolol 42 Neomercazole

17 Nikorandil 43 Betahistine HCL

18 Ondansetron 44 Kesol Syrup / Inj

19 Paracetamol 45 Vit - B12

20 Perindopril 46 Ethamysylate

21 Ramipril 47 Fenofibrate

22 Trimetazidine 48 Inj Calcium Gluconate

23 Streptokinase Inj 49 (Glucose + Insulin ) Drip

24 Taxim Inj 50 K-bind powder

25 Enoxaparin Inj 51 Syp. Gellusil / Mucaine gel

26 Rabeprazole Inj 52 Sucralfate Syp

ALL MEDICINES

31

Individual Patients Information regarding diseases and symptoms

Sr. No.

Patient

NameAge P1 P2 P3 P4 CVS RS PA CNS ECG BT

1 A 55M 2 1,2,13,5 4 7,10 8 4 5 4 1,3 14

2 B 58M 2 1,2,8 2 7,8,13,14 8 4 5 4 2 7,

3 C 60 M 8 13,7,5 4 1,6,12 8 4 5 4 9 14

4 D 60F 1,2 4,5 4 1,2,7,13,14,16 3,5 3 5 4 12 4

5 E 56F 1 15,16 4 6,9,12 8 4 6 4 10 2

6 F 33M 8 1,4 1 6,10 8 4 5 4 3 2

7 G 60 M 1 15,3,4 4 6,10,12 8 4 5 4 9 14

8 H 60M 1,7 1,17 4 6,9 6 4 5 4 9 9

9 I 50M 7 18 4 3,7,9,16 3 3 1 4 11,3 14

10 J 60F 9,2,3 4 4 7,9,13,18 3 3,5 5 4 11.3 9,10,4

11 K 47M 8 1,5 1 6,10 8 4 5 4 3 9,1,2

12 L 45F 8 1 4 19 8 4 5 4 1,9 14,2

13 M 60F 1,2,6 1,5,8 4 6,10,12,14 8 4 5 4 1,2 1,4,9,10

14 N 50M 1,7 4,3,5,6,15 2 3,7,10,13,17,18 3 3,5 5 4 4,11 15

15 O 75M 7,10 19 4 6,9,14 8 4 5 3 11,4 14

16 P 60F 1 1 4 6,9,12 8 4 5 4 10,9 14

17 Q 60F 1,2,7 3,5,6,7 4 6,9,12 8 4 5 4 9 3

18 R 42M 7 1 4 6,9,12 8 4 5 4 11,4 14

19 S 42M 4 2,5,6,13 4 6,10,12,17,20 8 4 5 5 1,4 4,9,16,17

20 T 55M 7 1,5,6,13 4 20,17,6,10,13 8 4 5 5 1,3,11,4 2,9,10,16,17

Symptoms and Findings

32

Sr. No. Patient

NameMID1 MID2 MID3 MID4 MID5 MID6 MID7 MID8 MID9 MID10 MID11 MID12 MID13 MID14 MID15 MID16 MID17

1 A 1 3 5 6 14 17 19 21 23 25 26 27 29 36

2 B 1 3 5 6 13 14 17 19 21 23 25 26 27 29 36

3 C 14 6 5 1 25

4 D 11 13 14 17 3 5 7 10 29 19 30

5 E 3 14 19 15

6 F 25 6 3 29 5 17 1 21 16

7 G 14 3 5 6 1 26

8 H 29 1 3 5 25 17 16 27 24

9 I 32 11 20 33 14

10 J 14 11 34 3 5 33 35 24

11 K 23 1 5 6 29 25 17 31 16 21 36

12 L 25 1 3 5 29 17 16

13 M 23 25 26 27 28 29 13 1 3 5 16 17 18 21 37 6 36

14 N 11 21 29 1 3 5 17 7 28 19 15

15 O 9 30 33 3 19

16 P 14 1 3 5 2

17 Q 38 3 14 5 1

18 R 25 2 3 5 6 29 17 16

19 S 2 3 5 6 13 14 17 19 21 23 25 26 27 28 29 36

20 T 2 3 5 6 14 16 17 19 21 25 26 27 28 29 36

Individual Patient Medicines prescribed by doctor

33

After the collection of ◦ 300 Patients information

the data is verified by doctor.

For further training of neural network process ◦ the proposed information is coded into

binary form (0 or 1).

If the symptom is present, ◦ it is represented by one (1) and

if the symptoms or disease is not present at that position it is represented by Zero (0).

34

Suppose for example in the field ◦ P2 (Present Disease)

there are total 29 symptoms

and the patient A is having the symptoms

1, 2, 5 and 13 so these locations are

defined by 1 (one) and

all other locations are defined by 0 (zero).

In such a way all the fields are defined.

◦ All the parameter

are converted in binary numbers

where this is used in neural network

to train the neurons for achieving better result.

35

Age 0110111 {7}P1 010000000000000000 {18}P2 11001000000010000000000000000 {29} P3 0001 {4}P4 0000001001000000000000000 {25} CVS 00000001 {8} RS 00010 {5}PA 000010 {6}CNS 00010 {5}ECG 101000000000000000000 {21} BI 000000000000010000000000 {24}

-------152 Inputs

0110111 010000000000000000 11001000000010000000000000000 0001 0000001001000000000000000 00000001 00010 000010 00010 101000000000000000000 000000000000010000000000

Fig. 3.1 Symptoms and Information Coding of patient A

And medicine given by the doctor for patient A is converted into binary form as shown in fig. 3.2

1010110000000100101010101110100000010000000000000000 (52 Outputs)

Fig. 3.2 Medicines coding of patient A

36

Proper diagnosis◦ is critical,

appropriate treatment the underlying cause.

So in order to present ◦ mathematical form,

a neural network with 152 inputs and 52 outputs

has been created.

37

Total number of neurons ◦ is equal to the number of

input parameters (152) and number of output parameters (52).

This network ◦ MATLAB 7.4

neural network toolbox version 5.0.2.

Out of 300 patient’s data, ◦ 250 were trained and

50 patient’s data were tested using all the following techniques.

38

Diagnosis of Heart Disease, ◦ proper interpretation of the

heart data besides

clinical examination and

complementary investigations

are considered in our work.

In this research work ◦ five ANN architectures were used such as,

Feedforward Back-Propagation Neural Network (BPN),

Generalized Regression Neural Network (GRNN),

Quasi Newton’s Algorithm,

Support Vector Machine (SVM) and

Radial Basis Function (RBF) Networks are used.

39

40

Step 1: Initialize weight to small random values

Step 2: While stopping condition is false, do

Steps 3-10

Step 3: For each training pair do Steps 4-9

Feed Forward

Step 4: Each input unit receives the input signal xi

and transmits this signals to all units in the above

layer i.e. hidden units.

41

Comparison of Results with Original Medicine

Sr

.

Results obtained by using

FFBP

Medicines prescribed by the

doctor

Extra

Medicine

Medicine not

prescribed

A 1,3,5,6,14,16,17,21,23,25,26,2

7,28

1,3,5,6,14,17,19,21,23,25,26,27,

29,36

16, 19,28

B 1,3,5,6,14,25 1,3,5,6,13,14,17,19,21,23,25,26,

27,29,36

Nil 13,17,19,21,23,25,26,

27,29,36

C 1,3,5,6,14,25 1,5,6,14,25 3 Nil

D 1,3,5,6,14,25 3,5,7,10,11,13,14,17,19,29,30 1,6 7,10,11,13,14,17,19,

30

E 1,3,5,6,14,25 3,14,15,19 1,5,6,25 15,19

F 1,3,5,6,14,25 1,3,5,6,16,17,21,25,29 Nil 16,17,21,29

G 1,3,5,6,14,25 1,3,5,6,14,26 25 26

H 1,3,5,6,14,25 1, 3, 5, 16, 17, 24, 25, 27, 29 14 16,17,24,27,29

I 1,3,5,6,14,25 11,14,20,32,33 1,3,5,6,25 11,20,32,33

J 1,3,5,6,14,25 3,5,11,14,24,33,34,35 1,6,25 11,24,33,34,35

47

Comparison of Results obtained by FFBP with Medicines prescribed by doctor

25/05/2016

Second International Conference on

Computer Technology, Kerla. 48

•Quasi-Newton search directions provide an attractive alternative

to Newton’s direction requiring only to satisfy the secant equation

•It is an iterative procedure

•They update an approximate Hessian matrix at each iteration of

the algorithm. The update is computed as a function of the

gradient

kkk ysB 1

Sr.. Results obtained by using Quasi

Newtons Algorithm

Medicines prescribed by

the doctor

Extra Medicine Medicine not prescribed

A3,4,8,11,12,14,16,20,24,26,27,28,29,32,

33,38,39,40,41,42,43,45,47,49,50

1,3,5,6,14,17,19,21,23,25,2

6,27,29,36

4,8,11,12,16,20,24,

28,32,33,38,39,40,4

1,43,45,47,

49,50

1,5,6,17,19,21,23,25,36

B

3,4,5,8,13,14, 19,22,26, 27,29,31,

36,37,38,41,42,43,44,45,46,48,50,

52

1,3,5,6,13,14,17,19,21,23,2

5,26,27,29,36

4,8,22,31,37,38,41,

42,43,44,45,4

6,48,50,52

1,6,17,21,23,25

C3,4,8,13,14,19,22,25,29,31,34,35,36,37,

41,44,45,46,48,50,521,5,6,14,25

3,4,8,13,19,22,29,

31,34,35,36,3

7,

41,44,45,46,4

8, 50,52

1,5,6

D3,4,8,13,14,19,22,25,29,31,34,35,36,37,

41,44,45,46,48,50,52

3,5,7,10,11,13,14,17,19,29,

30

4,8,22,25,31,34,35,

36,37,41,44,4

5,46,48,50,52

5,7,10,11,17,30

E3,4,8,14,19,25,29,31,34,35,36,37,41,44,

45,46,48,50,523,14,15,19

4,8,25,29,31,34,35,

36,37,41,44,4

5,46,48,50,52

15

F2,4,5,7,8,11,18,22,24,25,28,31,33,34,36,

37,38,39,40,43,44,45,47,49,501,3,5,6,16,17,21,25,29

2,4,5,7,8,11,18,22,2

4,28,31,33,34,

36,37,38,39,4

0,43,44,45,47,

49,50

1,3,5,6,16,17,21,29

49

Comparison of Results obtained by Quasi Newton’s Algorithm with Medicines prescribed by doctor

25/05/2016

Second International Conference on

Computer Technology, Kerla. 50

A GRNN is a variation of the radial basis neural networks, which isbased on kernel regression networks.

A GRNN does not require an iterative training procedure as backpropagation networks.

A GRNN consists of four layers: input layer, pattern layer,summation layer and output layer as shown in Fig. 4.5. Thenumber of input units in input layer depends on the totalnumber of the observation parameters. The first layer isconnected to the pattern layer and in this layer each neuronpresents a training pattern and its output. The pattern layer isconnected to the summation layer. The summation layer hastwo different types of summation, which are a single divisionunit and summation units. The summation and output layertogether perform a normalization of output set. In training ofnetwork, radial basis and linear activation functions are usedin hidden and output layers

Sr. Results obtained by using

GRNN

Medicines prescribed by the

doctor

Extra Medicine Medicine not

prescribed

A 1,3,5,6,14,16,17,21,23,25,26,27 1,3,5,6,14,17,19,21,23,25,26,27,2

9,36

19 29,36

B 1,3,5,6,11,14,16,17,21,23,24,25,

26,27,36

1,3,5,6,13,14,17,19,21,23,25,26,2

7,29,36

11,16 13,19,29

C 1,3,5,6,11,17,21,25 1,5,6,14,25 3,11,17,21 14

D 1,3,5,6,11,13,14,17,21,25 3,5,7,10,11,13,14,17,19,29,30 1,6,21,25 7,10,19,29,30

E 3,5,6,14 3,14,15,19 5,6 15,19

F 1,5,6,16,17,21,23,25,29,31,36 1,3,5,6,16,17,21,25,29 31,36 3

G 1,3,5,6,14,17,25 1,3,5,6,14,26 17,25 26

H 1,3,5,6,14,17,25 1, 3, 5, 16, 17, 24, 25, 27, 29 6 24,27,29

I 1,3,5,6,12,15,17,21,25,26 11,14,20,32,33 1,3,5,6,1215,17,

25,26

11,14,20,32,33

J 1,3,5,6,11,14,17,21,25,32 3,5,11,14,24,33,34,35 16,17,21,25,32 24,33,34,35

51

Comparison of Results obtained by GRNN with Medicines prescribed by doctor

linearly separable case, the decision rules defined by an optimal hyperplane separating the binary decision classes is given as the following equation in terms of

52

N

i

iii bxxysignY1

)(

25/05/2016

Second International Conference on

Computer Technology, Kerla. 53

Linearly separable statistical technique

The decision function for an SVM is fully specifiedby a subset of the data which defines the positionof the separator, These points are referred to asthe support vectors

The classification function is

N

i

iii bxxysignY1

)(

Sr

.

.

Results obtained by using

SVM

Medicines prescribed by the

doctor

Extra

Medici

ne

Medicine not

prescribed

A 1,3,5,6,16,17,18,21,23,25,26

,27,28,29

1,3,5,6,14,17,19,21,23,25,26,27

,29,36 16,18,28 14,1936

B 1,3,5,6,16,17,18,21,23,25,26

,27,28,29

1,3,5,6,13,14,17,19,21,23,25,26

,27,29,36 16,18, 13,14,36

C 1,3,5,6,11,14,21,22,23,24,25

,26,27

1,5,6,14,25 3,21,22,2324

,26,27Nil

D 1,3,5,6,13,14,17,21,22,23,25

,26,27,28

3,5,7,10,11,13,14,17,19,29,30 1,6,21,22,23,

25,26,2

7,28

7,10,11,19,29,30

E 1,2,3,5,14 3,14,15,19 1,2,5,14 15,19

F 1,2,3,6,13,14,16,17,21,25 1,3,5,6,16,17,21,25,29 2,13,14 29

G 1,2,3,8,11,14,25,26 1,3,5,6,14,26 2,8,11,25,26 5,6

H 1,3,5,6,10,11,14,17,22,25,27 1, 3, 5, 16, 17, 24, 25, 27, 29 6,10,11,14,22 16,29

I 1,3,5,6,10,11,14,20,24 11,14,20,32,33 1,3,5,6,10,24 32,33

J 1,3,5,6,11,14,24,33 3,5,11,14,24,33,34,35 1,6 34,35

54

Comparison of Results obtained by SVM with Medicines prescribed by doctor

25/05/2016

Second International Conference on

Computer Technology, Kerla. 55

It is a feed forward network with a single layer of hidden units called RBFs.

Its output shows the maximum value at its center points and decrease its output value as its input leaves the center

Calculate the output of the neural network

Where, H – number of hidden layer nodes (RBF function)ynet – output value of mth node in output layer for the nth incoming patternwim – Weight between ith RBF unit mth output nodewo – Biasing term of output node

56

1

( )H

net im i i o

i

y w v x w

Sr. Results obtained by using RBF Medicines prescribed by doctor Extra

Medicine

Medicine not

prescribed

A 1,3,5,6,14,16,17,21,23,25,26,27 1,3,5,6,14,17,19,21,23,25,26,27,29

,3616 19, 29,36

B 1,3,5,6,14,17,21,23,25,26,27,36 1,3,5,6,13,14,17,19,21,23,25,26,27

,29,36 Nil 13,19,29

C 1,5,6,14,25 1,5,6,14,25 Nil Nil

D 3,5,7,10,11,13,14,17,19,29,30 3,5,7,10,11,13,14,17,19,29,30 Nil Nil

E 1,3,5,6,14 3,14,15,191,5,6 15,19

F 1,5,6,16,17,21,23,29,31,36 1,3,5,6,16,17,21,25,2923,31,36 3,25

G 1,3,5,6,14,17,25 1,3,5,6,14,2617,25 26

H 1,3,5,6,14,17 1, 3, 5, 16, 17, 24, 25, 27, 296,14 16,25,27,29

I 1,5,11,14,20,24,25,32,33 11,14,20,32,33 1,3,24 Nil

J 1,3,5,6,11,14,17,21,32,33,34,35 3,5,11,14,24,33,34,351,6,17,21,32 24

57

Comparison of Results obtained by RBF with Medicines prescribed by doctor

Result

prescribed by

FFBP

Result

prescribed by

GRNN

Result

prescribed by

SVM

Result

prescribed by

Quasi

Result

prescribed by

RBF

Result

prescribed by

Doctor

1,3,5,6,14,16,17,2

1,23,25,26,27,28

1,3,5,6,14,16,17,2

1,23,25,26,27

1,3,5,6,16,17,18,2

1,23,25,26,27,28,

29

3,4,8,11,12,14,16,

20,24,26,27,28,29

,32,33,38,39,40,4

1,42,43,45,47,49,

50

1,3,5,6,14,16,17,2

1,23,25,26,27

1,3,5,6,14,17,19,2

1,23,25,26,27,29,

36

0110111 010000000000000000 11001000000010000000000000000 0001

7 24 29 4

0000001001000000000000000 00000001 00010 000010 00010

25 8 5 6 5

101000000000000000000 000000000000010000000000

21 25

Inputs given for patient A

1010110000000100101010101110100000010000000000000000

52

Medicines prescribed by the doctor to patient A

58

Comparison of Results obtained by each technique with Medicines prescribed by doctor

Discussion of First Eight Patients prescribed by RBF with doctor:

Patient A:◦ Medicine no. 16 is beneficial as it reduces the heart rate and thereby reduces

workload and improves outcome. ◦ Medicine no. 19 is a antipyretic drug (to reduce fever) or an analgesic if given,

will not affect the cardiac outcome. ◦ Medicine No. 29 is injectable form of medicine no. 14 which the system has

already prescribed. ◦ Medicine no. 36 is a laxative (Stool Softener) is given to patients who complain

passing hard stools which cannot be judged by the system.

Patient B :◦ Medicine no. 13 is insulin injection which is given to patients having high blood

sugar levels which must be prescribed by the expert system as per sugar levels. ◦ Medicine no. 19 is a antipyretic drug (to reduce fever) or an analgesic if given

will not affect the cardiac outcome. ◦ Medicine No. 29 is injectable form of medicine no. 14 which the system has

already prescribed.

59

Patient C: ◦ The system has prescribed as per the symptoms and physical examination which is justified.

Patient D: ◦ The system has prescribed as per the symptoms and physical examination which is justified.

Patient E: ◦ Medicine no 1 is alprazolam which is anxiolytic and is given to almost all patients and won’t affect the

heart patient.

◦ Medicine no. 5 is Atorvastatin which is a lipid lowering agent and helpful in decreasing cholesterol levels.

◦ Medicine no. 6 and Medicine no. 3 has the same effect.

◦ Medicine no. 19 is an antipyretic drug (to reduce fever) or as an analgesic if given will not affect the cardiac outcome.

Patient F: ◦ Medicine no. 6 has the same action as medicine no. 3 and is already prescribed.

◦ Medicine no. 23 is given only after interpretation of ECG and clinical judgment of patient by the treating physician and that too within first 24 hours of ECG changes which the system can’t identify.

◦ Medicine no. 31 is prescribed only in presence of Arrhythmias and has to be used cautiously.

◦ Medicine no. 36 is given, wont harm the patient’s outcome as it is a stool softener.

60

In this research work,◦ neural network

diagnosing proper disease and

medical prescription.

The network was ◦ trained with 250 patient’s data

and 50 patients were tested by using all the techniques.

It was found that Radial basis function

satisfactory medical prescriptions.

This research work◦ developing countries

where immediate expert and medical help is not possible.

61

Employing the technology, ◦ specially neural network techniques

in medical application could minimize the cost, time,

human expertise and

medical errors.

This expert system ◦ could prove useful for

paramedical staff and trainee doctors especially at remote places

where medical experts could not give services instantly.

Under those circumstances ◦ the paramedical staff or trainee doctors

can use this system as primary source to save patient life till medical expert arrives.

62

This research work developed by ◦ using Artificial Neural Network techniques

could be used to assist the doctors in making decision without consulting the specialists directly.

However medicine being a clinical branch, ◦ few drugs has to be

clinical parameters on the basis of judgment and clinical experience

of the treating physician which

cannot recognize

because of lack of vision.

It does not mean ◦ that this research work will

experienced and specialized doctors but the research work will help a

medical practitioner/paramedical staff

in facilitating to prescribe

proper medicines for heart disease.

63

In future this experiment

◦ will be extended to diagnose

the heart disease using ECG waveform

which will be taken directly by

computer system along with patient’s information.

Hope that such unique

◦ combination may increase more accuracy

64

69

70

Thank You..!

Welcome

71

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The perceptron itself consists of the weights, the summation processor and the threshold processor.

Learning is the process of modifying the values of the weights and the threshold.

A perceptron computes a binary function of its input.

Linearly separable means we can draw a line that separates one class from another.

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