Therapeutic Diet Prediction for Integrated Mining of Anemia Human Subjects using Statistical Techniques

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 8, November 2010

Therapeutic Diet Prediction for Integrated Mining of
Anemia Human Subjects using Statistical Techniques

Sanjay Choudhary Abha Wadhwa
Department of Mathematics & Computer Science Department of Computer Science & Application
Govt. Narmada P.G. Mahavidyalaya Govt Girls P.G. College
Hoshangabad, India Hoshangabad, India
schoudhari123@rediffmail.com abhahbd@gmail.com
Kamal Wadhwa Anjana Mishra
Department of Mathematics & Computer Science Department of Mathematics & Computer Science
Govt. Narmada P.G. Mahavidyalaya Govt. Narmada P.G. Mahavidyalaya
Hoshangabad, India Hoshangabad, India
wadhwakamal68@gmail.com anjanamishra10@yahoo.com

mining, which determines the correlation between items belonging
Abstract :- Chronic disease anemia [1] occurs when blood to transaction database [6], [7].
doesn’t have enough hemoglobin. Hemoglobin is a protein in red
blood cells that carries oxygen from lungs to the rest of our Chronic disease is a disease[10] that is long lasting for recurrent.
body. All the body parts need oxygen. Anemia can starve our Anemia also becomes a chronic disease if it is not cured timely. It is
body of the oxygen it needs to survive. Possible causes of anemia prolonged, do not resolve spontaneously and are rarely cured
include low vitamin B12 or folic acid intake and some chronic completely. If anemia results from a diet which is low in iron, iron
illnesses. But the most common cause is not having enough iron rich foods or iron pills may be the doctor suggests. Data Mining
in blood which needs to make hemoglobin. This type of anemia technology can be utilized for improving the quality of health care
is called iron deficiency anemia. of an individual. There is need for lowering the cost of health care
facilities along with quality based treatment especially for poor
Data Mining is widely used in database communities because sections of our society.
of its wide applicability. One major application area of Data
Mining is in therapeutic diet prediction. There are several Health care organization need to lower cost, raise quality and still
chronic diseases which can be prevented using nutritive food. remain competitive. IT can be used for patient health care. In the IT
This paper presents association and correlation between anemia driven society the role of IT in health care is well established. We
human subject and its prevention through diet nutrients. The can use data mining techniques for analyzing patient data to
role of diet in preventing and controlling iron deficiency is generate predictions and knowledge which can in turn be used for
significant. Due to changes in dietary and life style patterns fast and better clinical decision making. Medical data mining deals
anemia can become catastrophic, so by predicting proper and with large amount of data, there fore we need data mining
sufficient diet nutrients for individuals, we can reduce the techniques, which can explore hidden patterns in the data sets of the
impact of anemia on human subjects. medical domain. These patterns can be utilized for clinical diagnosis
and prediction.
Keywords :- Chronic Disease, Anemia, Diet Nutrients, Clinical
System, Correlation, Data Mining The available raw medical data are widely distributed,
heterogeneous in nature and voluminous, therefore these data need
to be collected in an organized form. This collected data can be then
I. INTRODUCTION
integrated to form as a basis for prediction of nutrients chronic
diseases. Data mining technology provides a user oriented approach
Data Mining is referred to as Knowledge Discovery from to extract hidden patterns from the data. Data mining can deal with
databases[5], a process of nontrivial extraction of implicit previously heterogeneous type of data. Medical data are different from data in
unknown and potentially useful information from databases, has other databases. Medical data are heterogeneous and contain text
wide application in information management concepts, query and images also, for example MRI , ECG, etc generate huge
processing, decision making, process control, statistical analysis etc. amounts of heterogeneous medical data. Knowledge discovery from
[4], [5] An association rule mining is an important process in data this type of data can greatly benefit mankind by improved diagnostic
techniques.

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 8, November 2010

A. Background II. ASSOCIATION AND CORRELATION TO BUILD
RELATIONSHIP
Diet and nutrition are important factors in the promotion and
maintenance of good health throughout the entire life course. The In this paper we are dealing with two distinct systems, anemia
chronic diseases related to diet and nutrition are anemia, obesity, human subject and diet prediction. Based on the level of hemoglobin
diabetes, cardiovascular disease, cancer, osteoporosis etc. Anemia of individual, we have made an attempt to associate human subject
can be defined as a reduction in the hemoglobin, hematocrit or red and iron requirement. It has been found that average range of
cell number[10]. The sudden, rapid loss of 30% of the total blood hemoglobin for males is 14-16 and for females is 12-14. The iron
volume often results in death. The burden of chronic disease is requirement for average range of hemoglobin individual according
rapidly increasing world wide .It has been calculated that in 2001, to RDA (Recommended Dietary Allowances) table is 28mg for
chronic diseases contributed approximately 60% of the 56.5 million males and 30mg for females. The table given below depicts the fact
total reported deaths in the world and approximately 46% of the that the iron requirement of females is more than that of males. We
global burden of disease. have used the correlation statistical technique for finding the
relationship between both the systems.
The chronic disease problem has effected a large proportion of
our population. It has been projected that by 2020 chronic diseases In every case of anemia[9], the cause should be discovered and
will account for almost three quarters of all deaths worldwide are treated. In clinical practice, nutritional anemia is commonly
due to heart diseases. Chronic diseases are largely preventable associated with overall under-nutrition and a balanced diet should be
disease. Research has shown that diet can control chronic diseases to given. Usually, diet alone is not adequate and therapy with specific
large extent[11]. Modern dietary patterns and physical activity supplements – particularly iron – is also needed. Supplements of
patterns are beneficial in prevention of chronic diseases. The 10mg (179 micromol) iron daily prevent iron deficiency.
chemical composition of food and physiological response to diet can Association rule mining[5] uses support confidence framework.
be discovered. Unraveling the interconnection between diet and Diet can cure anemia, this statement can be justified through support
health through data mining techniques is the basis of this paper confidence measure. We can augment support confidence
identification of disease and diet associated composite molecular framework through correlation. The Bayesian correlation technique
biomarkers will facilitate identification of new molecular targets for has been used to find the correlation between required level of iron
development of novel therapeutic agents to address diet related (in mg) and range of hemoglobin of individuals.
chronic diseases .
III. EXPERIMENTAL RESULTS
B. Data Mining And Statistics
Sufficient safe and varied food supplies not only prevent
Data Mining is designed[2] to learn future prediction. The Data malnutrition but also reduce the risk of chronic diseases whereas
Mining tool checks the statistical significance of the predicted nutritional deficiency increases the risk of common infectious
patterns and reports. The difference between Data Mining and diseases generally in children. This paper has used data mining
statistics is that Data Mining automates the statistical process techniques to investigate factors that contribute significantly in
requiring in several mining tools. Statistical inference is assumption reducing the risk of chronic disease anemia.
driven in the sense that a hypothesis is formed and tested against
data. Data Mining in contrast is discovery driven. That is, the There are two methods of analyzing correlation between
hypothesis is automatically extracted from the given data. The other variables:
reason is Data Mining techniques tend to be more robust for real-
world data and also used less by expert users.
(i) Karl Pearson’s Method
(ii) Scatter Diagram Method
C. Statistical Correlation
(i) Use of Karl Pearson’s method to find coefficient of correlation:
Through correlation[3] we may find that a change in one variable Correlation is the relationship between two or more than two
result in change of second variable. variables. We are finding correlation between anemia human
subjects(male and female) and predicted diet. Anemia is a chronic
Whenever there exists a relationship between two variable such disease caused due to several factors. Among those factors
that a change in one variable results in a positive or negative change inadequate and malnutrition is a major cause of anemia. We have
in other and also greater change in one variable results in a generated the following correlation Table 1 for male anemia subject
corresponding greater change in the other, the relationship is called with mid value of hemoglobin range(Xm) and iron intake (Ym mg):-
correlation and the two variables are called correlated.
Hb range: 0-6 6-8 8-12 12-14 14-16
Two variables are called positively correlated if corresponding to
an increase (or decrease) in one variable results in an increase (or Iron intake: 42 36 34 31 28
decrease) in the other.
TABLE 1.
Two variables are negatively correlated if corresponding to an
x2 y2
increase (or decrease) in one variable results in decrease (or Xm Ym x=Xm- y=Ym x*y
increase) in the other. Mx -My
3 42 -6.6 7.8 43.56 60.84 -51.48
7 36 -2.6 1.8 6.76 3.24 -4.68

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(IJCSIS) International Journal of Computer Science and Information Security,
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10 34 0.4 -0.2 0.16 0.4 -0.8 TABLE 2.
13 31 3.4 -3.2 11.56 10.24 -10.88
x2 y2
15 28 5.4 -6.2 29.16 38.44 -33.48 Xf Yf x=Xf- y= Yf x*y
48 171 Σx2= Σy2= Σx*y = Mx -My
91.2 113.16 -101.32 3 45 -5.25 8.5 27.56 72.25 -44.625
7 38 -1.25 1.5 1.56 2.25 -1.875
MXm = Σ Xm /n = 48/5 = 9.6 10 33 1.75 -3.5 3.06 12.25 -6.125
13 30 4.75 -6.5 22.56 42.25 -30.875
MYm = Σ Ym /n = 171/5 = 34.2 33 146 Σx2= Σy2= Σx*y=
54.74 129 -83.5
The coefficient of correlation for male anemia subjects:-

r = Σ x*y/√ Σ x2* Σ y2
MXf = Σ Xf /n = 33/4 = 8.25
= -101.32/√(91.2)*(113.16)
MYf = Σ Yf /n = 146/4 = 36.5
= -101.32/√ (10320.192)

= -101.32/101.5883
The coefficient of correlation for female anemia subjects:-
= -0.9974
r = Σ x*y/√ Σ x2* Σ y2

= -83.5/√(54.74)*(129)
(ii) Scatter Diagram Method: The diagrammatic representation of a
bivariate data is known as scatter diagram. For bivariate the values = -83.5/√ (7061.46)
of variables X and Y are plotted (male and female) in the X-Y plane.
X axis denote range of hemoglobin and Y axis denotes iron required
for individuals. = -83.5/84.0325

Figure 1 = -0.9937

Correlation between Hemoglobin and Iron Intake
(Male) Figure 2

50 Correlation between Hemoglobin and Iron Intake
40
Iron Intake

(Female)
30
Iron Intake
20 50
10 40
Iron Intake

0
30
0 5 10 15 20 Iron Intake
20
Hem oglobin Range
10

0
0 5 10 15
In this scatter diagram all the points lie on a line and it declines Hem oglobin Range
from left to right, this depicts the fact that there is a negative
correlation of high degree between the variables, i.e. if the
hemoglobin count of individual is less than the required iron is
more. If measure of correlation is between -0.75 and -1, negative then
measure of correlation is of high degree.
We have generated the following correlation Table 1 for female
anemia subject with mid value of hemoglobin range(X) and iron Both the methods have justified the same fact that there is
intake (Ymg):- negative correlation of high degree between hemoglobin range and
required iron.
Hb range: 0-6 6-8 8-12 12-14
IV. CONCLUSION AND FUTURE SCOPE
Iron intake: 42 36 34 30
Data Mining can be performed by dietitians and hospital
administration to prepare diet chart for anemic patients. The goal is

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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 8, November 2010

to detect when the Hb percent of patient is very low the diet [6] I. Ha, Y. Cai, and N. Cercone, “Data-driven of Quantitative
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Nutrition”, 329-330, 1997.
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areas which suffer from some kind of deficiencies such as Calcium services. International Journal of Medical Informatics
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and justify the fact that there is high degree of correlation between
hemoglobin range of individual and iron intake. By using this fact
organizations can plan diet patterns for areas facing the problem of
anemia. The entire population of that area can be prescribed iron
rich food. Dietitians can also use this fact for preventing this
common disease of particular geographical area. There are several
remote areas in India which suffer from this common disease.
Organisations can directly plan nutrients for those needy people
without the need for performing unnecessary calculations. This
result will be beneficial for large masses of people. They can be
prevented from this chronic disease by giving iron rich food, so the
above results are beneficial for our society.

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