Hepatitis is deadly, and the fifth leading cause of death after heart disease, stroke, chest disease and cancer. Worldwide, 1.5 million deaths per year have been estimated. Detection of hepatitis is a big problem for general practitioners. An expert doctor commonly makes decisions by evaluating the current test results of a patient or by comparing the patient with others with the same condition with reference to the previous decisions. Many machine learning and data mining techniques have been designed for the automatic diagnosis of hepatitis. However, no one tool is available to the general population for the diagnosis of Hepatitis. Hence, a graphical user interface-enabled tool needs to be developed, through which medical practitioners can feed patient data easily and find hepatitis diagnoses instantly and accurately. &#xd;&#xa;Methods: In this study a hepatitis dataset was taken from the UCI machine repository database with a total of 20 attributes of two classes, Affected and Not Affected. &#xd;&#xa;Results and Conclusion: The models have been generated with a mixture of experts as a classification method for the diagnosis of hepatitis. Very good accuracy has been observed in the generated models. Finally, the model having the least minimum square error was selected. This model was then linked with GUI for the design of tools for hepatitis prediction

Ambarish S. Vidyarthi

M Shankaracharya

Suchitra Kumari

English

AbstractHepatitis is deadly, and the fifth leading cause of death after heart disease, stroke, chest disease and cancer. Worldwide, 1.5 million deaths per year have been estimated. Detection of hepatitis is a big problem for general practitioners. An expert doctor commonly makes decisions by evaluating the current test results of a patient or by comparing the patient with others with the same condition with reference to the previous decisions. Many machine learning and data mining techniques have been designed for the automatic diagnosis of hepatitis. However, no one tool is available to the general population for the diagnosis of Hepatitis. Hence, a graphical user interface-enabled tool needs to be developed, through which medical practitioners can feed patient data easily and find hepatitis diagnoses instantly and accurately.
              Methods:
              In this study a hepatitis dataset was taken from the UCI machine repository database with a total of 20 attributes of two classes, Affected and Not Affected.
            
              Results and Conclusion:
              The models have been generated with a mixture of experts as a classification method for the diagnosis of hepatitis. Very good accuracy has been observed in the generated models. Finally, the model having the least minimum square error was selected. This model was then linked with GUI for the design of tools for hepatitis prediction.</jats:p

Ambarish Vidyarthi

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Development of Java based graphical user interface for Diagnosis of Hepatitis UsingI Mixture of Expert

DEVELOPMENT OF JAVA BASED GRAPHICAL USER INTERFACE       
FOR DIAGNOSIS OF HEPATITIS USING MIXTURE OF EXPERT       
Shankaracharya*, Suchitra kumari , Ambarish S.Vidyarthi
Department of Biotechnology Birla Institute of Technology Mesra Ranchi 835215 India  ,    , ,  , .
*Add d t Sh k h il h k h @bit iress correspon ence o: an arac arya, e-ma : s an arac arya mesra.ac. n
Presenting author: Suchitra kumari, e-mail: suchibiotech27@gmail.com      
ABSTRACT RESULTS
H titi i d dl d fifth t d th i di ft h t di t k h t di We get high accuracy in diagnosis of hepatitis with Mixture of Expert method. Total eightepa s s a ea y an mos ea caus ng seases a er ear sease, s ro e, c es sease
d ld id illi d h h b i d h i f h i i i parameters were considered in the development of different models in this method Out of which sixan cancer. Wor w e, 1.5 m on eat s ave een est mate eac year. Detect on o epat t s s .
parameters were kept constant and two were kept variable to define various cases Following are thereally a big problem for general practitioners. An expert doctor commonly takes decisions by .
8 tevaluating the current test results of a patient or by comparing the patient with other patients with parame ers :
the same condition with reference to the previous decisions Many machine learning and data No. of hidden units for the expert MLPs, No. of expert, No. of hidden units for the gate Multi Layer.
mining techniques have been designed for the automatic diagnosis of hepatitis However no any Perceptron, Expert output activation function, Type of ME, The no. of iterations in the M-Step,. ,
l i il bl h l l i f h di i f H i i H G hi l Optimization in the M-step, Expectation Maximization (EM).too s ava a e to t e genera popu at on or t e agnos s o epat t s. ence, rap ca user
The highest accuracy of 98 18% was achieved while considering the following parameters (Table 1)interface-enabled tool needs to be developed through which medical practitioners can feed patient .
data easily and find the hepatitis diagnose instantly and accurately. Methods: In this study hepatitis
dataset taken from UCI machine repository database in which total 20 attributes having two classes,
Affected and Not Affected Result and Conclusion: The models have been generated with Mixture.
f t l ifi ti th d f th di i f h titi V d h bo exper s as a c ass ca on me o or e agnos s o epa s. ery goo accuracy as een
observed in the generated models. Finally the model having least minimum square error was
selected. This model was then linked with GUI for the design of tool for hepatitis prediction.
Ke ords H titi Di i P bl f P titi UCI Mi t f E t Al ithyw : epa s agnos s, ro em or rac oners, , x ure o xper , gor m.
INTRODUCTION
Fig 3 : Showing iteration with best result model   
Hepatitis is the fifth most death causing diseases after heart disease stroke chest disease and cancer, ,
[Mougiakakou et al 2009] The word hepatitis simply means an inflammation of the liver without, .
i i ti (htt //kid h lth / t/i f ti /b t i l i l/h titi ht l) V i i k
Table 1: MSE result
p npo n ng p: s ea .org paren n ec ons ac er a -v ra epa s. m . ar ous r s
f f h i i i l d bl d f i d i i d b h di l i S O i i i i i i S S f A iactors or epat t s nc u es oo trans us ons, tattoos an p erc ng, rug a use, aemo a ys s, . No. pt m zat on n
th M St
Expectat on
M i i ti
M n . quared
E (MSE) f
M E or test
d t
ccuracy Matr x
health workers and sexual contact with hepatitis carrier e - ep ax m za onstep
rror or 
train data
a a
[http://www.angelfire.com/biz2/physician29/hepatitis.html].
 
1. Scaled gradient 200 0.000000 0.036248 92.73% 5 2
Early stage diagnosis of hepatitis is very difficult in general population due to the lack of regular 2 46
i h k d Th f di l di i i i diffi l d ll d drout ne c ec up an awareness. ere ore, me ca agnos s s qu te cu t an tota y epen s 2. Scaled gradient 200 0.000000 0.013742 98.18% 6 1
on visual task done by expert doctors based on their expertise. An expert doctor commonly takes 0 48
decisions by evaluating the current test results of a patient or compares the patient with other 3 C j t 200 0 000000 0 006081 98 18% 6 1
patients with the same condition by referring to the previous decisions [Sartakhti et al 2011] For
. on uga e
gradient
. . .
0 48, .
this reason so many machine learning and data mining techniques have been designed for the
t ti di i f h titi Th d t f th h i di l di i 4. Conjugate 300 0.000000 0.013742 98.18% 6 1au oma c agnos s o epa s. e a van ages o ese approac es n me ca agnos s may gradient 0 48
result in decreased cost and increased diagnosis accuracy.
5 Conjugate
i
800 0.000001 0.018146 96.36% 6 1
1 4grad ent 7
METHOD Table 2.  Confusion 
Matrix
Mixture of expert (ME) models (Fig. 1) comprise of a gating network that divides the problem into Without With 
smaller problems and expert networks that solve each sub problem [Titsias and Likas 2002] The hepatitis hepatitis, .
global output of the ME system is derived as a convex combination of the outputs from a set of N
t i hi h th ll di ti f f th t i ll i t th t f
Without
h i iexper s, n w c e overa pre c ve per ormance o e sys em s genera y super or o a o epat t s
6 1any of the individual experts . The standard choices for gating and expert networks are generalized
linear models and multilayer perceptrons. [Shankaracharya et al., 2012]
. Fi 5 T t
With 
h titiFigure  4.Train gure_ . es  epa s 0 48
graphical result graphical result  
CONCLUSIONS
Mixture of Expert based tool can help the physician for the easy cost effective fast and accurate, ,
h titi di i I f t t di th h titi di i di thi th d b f thepa s agnos s. n u ure s u es on e epa s agnos s seases, s me o can e ur er
difi d h l b i d b i i h b fmo e to get more accuracy. T e accuracy may a so e ncrease y ncreas ng t e num er o
subjects in the dataset.
REFERENCES
Fi 1 Mi t f E t A hit tg : x ure o  xper  rc ec ure
1 Adeli M and Zarabandipour H 2011 Automatic disease diagnosis systems using pattern. . . .
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3 htt //kid h lth / t/i f ti /b t i l i l/h titi ht l. p: s ea .org paren n ec ons ac er a -v ra epa s. m
4. http://www.angelfire.com/biz2/physician29/hepatitis.htmlRemoval of  instances with missing attribute and normalization of data
5. Mougiakakou G.S .2009. Diagnostic Support Systems and Computational Intelligence:
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10 4018/978 1 60566 314 2 ch005. - - - - .
6 Sh k h Od d D S t S Vid thi A S 2011 C t ti l I t lli iSplitting of data set into two group – training and testing data set . an arac arya, e ra ., aman a ., yar . . . ompu a ona n e gence n
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ar y ia etes iagnosis: eview; ev ia et tu ; : -
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Model. Neural Computation, 14:2221–2244Generation of different models by using matlab and Mixture of Expert method           
S l ti f b t ( t t h i l t MSE) d l ACKNOWLEDGEMENTe ec on o  es  mos  accura e av ng owes   mo e
Designing of a java base Graphical User Interface tool and connection of this GUI to the Matlab
The authors are grateful to the Sub-Distributed Information Center (BTISnet SubDIC) Department
                 
,
of Biotechnology (No BT/BI/04/065/04) New Delhi India and to the Department of. , , ,
Bi t h l Bi l I tit t f T h l M R hi f idi t ft do ec no ogy, r a ns u e o ec no ogy, esra, anc , or prov ng access o so ware an
i f f ili f h d
Fig 2: Overall block diagram of the methodology used
n rastructure ac ty or t e present stu y.
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