data mining in disease management

8/6/2019 Data Mining in Disease Management

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Data Mining in Disease Management

(Diabetic patients)

By:

Catherine M. Catamora

097699


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What is diabetes?

a metabolism disorder - the way the bodyuses digested food for growth andenergy.

a chronic disease that occurs either whenthe pancreas does not produce enoughinsulin or when the body cannoteffectively use the insulin it produces.


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Key facts

According to World Health Organization:

More than 220 million peopleworldwide have diabetes.

In 2004, an estimated 3.4 millionpeople died from consequences ofhigh blood sugar.

More than 80% of diabetes deathsoccur in low- and middle-income

countries. WHO projects that diabetes death

will double between 2005 and2030.


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In the Philippines:

According to Pediatric endocrinologistSioksoan Chan-Cua

The numbers of diabetic patientsare still low compared with other

countries.


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This study aims to use data miningtechniques in learning more about theinstances of diabetic patients underBernardos clinic in Bulacan.

The study although focuses on a smallsample of patients, tries to come up withfactors and combination of factors that

results to being diabetic.


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Related Studies

Hyperglycemia and type 2 diabetes amongFilipino women in the Philippines,Hawaii, and San Diego by the followingauthors:

Maria Rosario G. Araneta,a* DeborahJ. Morton,a Lina Lantion-Ang,bAndrew Grandinetti,c Mary Anne Lim-Abrahan,b Healani Chang,c ElizabethBarrett-Connor,a Beatrice L.

Rodriguez,d and Deborah L.Wingarda


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Methodology

Data Gathering Based from the patient records of

Bernardos Clinic

Data was inputted in a Excel file, later

converted into a csv file for WEKAprocessing.

WEKA Data was processed using Nave Bayes

classifier

Classification Technique PRISM algorithm was applied


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Raw Data Set


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Processing applied

Attributes used

Age

Gender

BMI Blood pressure

Cholesterol level

Data set was divided into 2 groups Male

Female


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BloodPressure

Chart


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Cholesterol Classification

Diabetic Age Classification


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Male Weight Classification

Female Weight Classification


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Results and Analysis

WEKA (Nave Bayes applied) findings Accuracy - 78.333%


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Using WEKA ~ NaveBayesclassifier


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Applying Prism Algorithm

Dataset were divided into two (gendergroups)


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Rules generated under theFemale Group:

First rule: regardless of age, cholesterollevel and body mass index: if a patienthas a blood pressure equivalent toModerate Stage, then the patient has

diabetes.


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New Rule: If Blood Pressure = Hypertension HighBP and ?, Then Diagnosis = diabetic

New RULE: if

Blood Pressure = Hypertension High BPAge Class = ElderlyCholesterol = High or Cholesterol = Very HighBMI = Overweight or BMI = Obese

ThenDiagnosis = Diabetic


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New Rule: if Cholesterol = Very High and ?, ThenDiagnosis = diabetic

New RULE: ifCholesterol = Very HighAge Class = ElderlyBlood Pressure = Normal, High Normal, Hypertension High and Moderate StageBMI = Normal, Overweight or ObesethenDiagnosis = Diabetic


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New Rule: if BMI = Overweight and ?, ThenDiagnosis = diabetic

New RULE: ifBMI = OverweightAge Class = ElderlyBlood Pressure = High Normal, Hypertension High and Moderate StageCholesterol = Borderline High, Very High and Very HighthenDiagnosis = Diabetic


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Rules generated under theMale Group:

First Rule: Regardless of age, cholesterollevel and body mass index: if a patienthas a blood pressure equivalent toModerate Stage and BMI is equal to

Obese then the patient has diabetes.


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New Rule: if Cholesterol = High and ?, ThenDiagnosis = diabetic

New RULE: ifCholesterol = HighAge Class = ElderlyBMI = OverweightBlood Pressure = Normal, High Normal or Hypertension BPthenDiagnosis = Diabetic


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New Rule: if Blood Pressure = Hypertension High BPand ?, Then Diagnosis = diabetic

New RULE: if

Blood Pressure = Hypertension BPAge Class = ElderlyCholesterol = Borderline High, High and Very HighBMI = Overweight or ObesethenDiagnosis = Diabetic


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New RULE: if Weight = Overweight and ?, ThenDiagnosis = diabetic

New RULE: ifBMI = OverweightAge Class = ElderlyBlood Pressure = High Normal or Hypertension BPCholesterol = Borderline High, High and Very HighthenDiagnosis = Diabetic


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Conclusion

Data mining technologies can haveimportant utility in diabetes mellitus andother disease management.

Fasting blood glucose test is the preferred

test for diagnosing diabetes. Diagnosis of diabetes can be made based

on any of the following algorithm results.

Data mining can be used to manipulate

data especially if its in electronic form..


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Data mining approaches can be appliedsuccessfully in data analysis, andmonitoring of diabetic and non-diabeticpatients.

Diabetes, once diagnosed, a lifetimetreatment is necessary, but with the helpof data mining and scientific researches,physicians can be equipped with a toolto prevent the worst case scenarios.


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Further Studies

It is suggested by the researcher that, datamining techniques for clustering andassociation could also be done tofurther analyze and study such dataset.

Taking sample data based from a dailymonitoring or any regular monitoringscheme of patients blood pressure,cholesterol level and BMI to accuratelyproduce a good dataset.


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Diabetes management in rural areas suchas Bulacan has a promising researchand application area. Further studiescan also be made to similar locations in

other provinces where small clinicsattend to a huge number of patients ona regular basis.

data mining in disease management

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