DRS is a recommender system for diabetes that provides diagnosis, recommends drugs, and provides dietary advice. It uses semantic web and data mining techniques for accurate results. The system architecture includes ontologies for drugs, medical tests, foods, and patient information. It uses a decision tree model built with the J48 classifier to diagnose diabetes from patient data. Drug and diet recommendations are made by querying the ontologies and rules using SWRL and SQWRL.

1. Diabetes Recommender System
)DRS)
(Using Semantic Web and Data Mining
techniques)
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2.  Nesma Mahmoud Saad Eddin
 Nada Hassan Naem
 Basma Gamal El-serafy
 Hanan Sabery Hamed
 Menna Talla Mamdouh Saoud
Subervised by
Dr. Heba Elbeh
Team Members
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3. 1. What is DRS?
2. Why is DRS?
3. System Architecture & Components
4. Diabetes Diagnosis (using DM)
5. Drug & Diet Recommendation (using SW)
6. How DRS Works
7. DRS Tools & Techniques
Agenda
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4.  DRS (Diabetes recommender system) is a web-
based recommendation system for diabetes. It provides
diagnosis for diabetes, recommends diabetics drugs,
recommends and advice diabetes.
1.What is DRS?
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5.  DRS is based on Data Mining and Semantic Web techniques
provides more accuracy in results.
 DRS provides more than one service (diabetes diagnosis, drug
recommendation, diet recommendation)
 DRS maintain personalization for users(pateints).
 Diabetes is one of the most chronic diseases in recent years.
 The number of drugs increased the number of patients increased.
2. Why DRS?
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6. 3. DRS Architecture and Components.
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7. 4. Diabetes Diagnosis
(Using Data Mining)
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8. Data Mining
 Data Mining Steps
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9.  Data Collection & Preprocessing
- Diabetes Database from UCI Machine Learning .
- It Contains 768 record samples records with 8 attributes
(e.g., age, number of pregnant, Plasma Glucose test, etc.)
- Pre-processing  replacing missing values, convert
numeric values to nominal, discretize attributes.
 Model Building technique
- We use decision tree for data mining classification.
- we use J48 Classifier for building the
model(Implementation of Decision tree).
 Evaluation & Deployment
- Accuracy of the classifier is about 75.78%.
Data Mining Steps
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10.  J48 Classifier is training and testing the preprocessed diabetics
data set.
 Producing a model in the form of tree and we use this model in
our DRS system for Diagnosing new users (diabetic or not)
 The resulting classification
tree model:
Classifier Model Building
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11. 5. Drugs & Diet
Recommendation
(Using Semantic Web)
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12.  Evolving extension of WWW in which web content :
- Can be expressed not only in natural language.
- But also can be understood, interpreted and used by software
agents.
- Permitting software agents to find, share and integrate
information easily.
 The idea of having data on the Web
- Defined and linked in such a way.
- Can be used by machines not only for display purposes, but
for automation, integration and reuse of data across various
applications
Semantic Web (SW)
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13. • Semantic Web Cake Layer.
SW Knowledge Representation &
Layers
Ontology
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14. • Why Ontologies are important
- Ontological analysis clarifies the structure of knowledge.
- Ontologies enable knowledge sharing.
• Ontology Definition:
Formal , explicit specification of a shared conceptualization
Ontology
Machine
readable
Concepts, properties,
functions, axioms
are explicitly defined
Consensual
knowledge
Abstract model of
some phenomena
in the world
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15.  Ontology Main Components
Ontology (cont.)
Person Country
Class (concept)
Animal
Individual (instance)
Belgium
Paraguay
China
Latvia
Elvis
Hai
Holger
Kylie
S.Claus
Rudolph
Flipper arrow = relationship
label = Property
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16. Determine
Scope
Data Gathering
&Consider Reuse
Enumerate
Terms
Define Classes
& Class
Hierarchy
Define
Properties
Check for
Anomalies
Step by Step Building Ontology
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17. Anti-diabetic drugs ontology (an ontology that
maintain ant-diabetic drugs).
Patient tests ontology ( an ontology that maintain
diabetes tests).
Diabetes food ontology (an ontology that maintain all
foods for diabetics diet).
Personal Patient Information ( ontology for patients’
personal and medical information)
DRS Ontologies
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18. • Define Scope: anti-diabetics drugs ontology for drug
recommendation for diabetics Patients in DRS system.
• Data Gathering & Consider reuse: reusing drugs ontology,
collecting data from internet and asking specialists.
• Enumerate Terms: List all nouns and verbs used in the
domain like (drug, drugs classes, drugs names, has
consideration, has dose time, etc.)
• Define Classes: nouns become classes in the ontology.
Anti Diabetic Drugs Ontology
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19. • Define classes hierarchy: define classes in taxonomic
(subclass) hierarchy.
Anti Diabetic Drugs Ontology(cont.)
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20. • Define Properties: define relationships .
• Data property: link individual to individual.
• Object property: link individual to literal.
Anti Diabetic Drugs Ontology(cont.)
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21. • Define Scope: represents diabetics patient tests and used
for drug recommendation with the drug ontology in DRS
system.
• Data Gathering & Consider reuse: reusing patients tests
ontology, collecting data from internet and asking
specialists.
• Enumerate Terms: List all nouns and verbs used in the
domain like (test, test name, test classes, has normal has
level, etc.).
• Define Classes: nouns become classes in the ontology.
Patient Tests Ontology
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22. • Define classes hierarchy: define classes in taxonomic
(subclass) hierarchy.
Patient Tests Ontology(cont.)
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23. • Define Scope: represents foods that allowed for diabetic
patients and knowledge about foods and used for diet
recommendation in DRS system.
• Data Gathering & Consider reuse: reusing foods ontology,
collecting data from internet and asking specialists.
• Enumerate Terms: List all nouns and verbs used in the
domain like (test, test name, test classes, has normal has
level, etc.).
• Define Classes: nouns become classes in the ontology.
Diabetes Foods Ontology
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24. • Define classes hierarchy: define classes in taxonomic
(subclass)
Diabetes Foods Ontology (cont.)
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25. • Define Properties: define relationships .
• Data property: link individual to individual.
• Object property: link individual to literal.
Diabetes Foods Ontology (cont.)
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26. • Define Scope: represents all patient information from
personal information to drug and diet information that use
to manage in DRS system.
• Data Gathering & Consider reuse: reusing ontology
personal ontologies and gathering all data needed for a
patients.
• Enumerate Terms: List all nouns and verbs used in the
domain like (patient name, age , allowed calories, etc.)
• Define Classes: nouns become classes in the ontology.
Patient Information Ontology
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27.  Define classes hierarchy: define classes in taxonomic
(subclass) hierarchy.
Patient Information Ontology
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28.  SWRL  Semantic Web Rule Language
 SWRL is a rule language for semantic web
 All rules are expressed in terms of OWL concepts (classes,
properties, individuals).
 Rule Example:
 We use in writing the diabetes medication rules.
SWRL for Medication Rules
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29.  We used the SWRL in DRS to represent the relationship
between diabetics important tests and suitable drugs.
 Diabetics medication rules:
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SWRL for Medication Rules

30. 6. How DRS Works?
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31. DRS Diabetes Diagnosis
Users
User Interface
Form
Weka API
1.Get user
information
2. Load Model
3. Classify User
4. Return Result
back to user
Classifier
Model
- Stored in
DRSLoading
model
Submit
Check
result
Passing
user
data
Model
Result
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32. DRS Drug recommendation
Protégé API
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Jess
Engine
Patient input
Recommended
Drug or patient
Diabetics
Users
Submit
Bridge
Drugs Ontology
Patients
Tests ontology
SWRL
Medication
Rules
Loading

33. DRS Diet Recommendation
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Diabetics
Users
SQWRL
Queries
Foods Ontology
Patients Informa
Ontology
Protégé API
Jess
Engine
Recommended
Personal meal
Bridge
LoadingFavorite Foods
& Personal
Information

34. 12/8/2015DRS 34
7. DRS Tools & Techniques

35. Thanks For Listening

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36. Questions?

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