DRS Presentation

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Diabetes Recommender System

Diabetes Recommender System)DRS)(Using Semantic Web and Data Mining techniques)7/5/2015DRS 1

Nesma Mahmoud Saad EddinNada Hassan NaemBasma Gamal El-serafyHanan Sabery Hamed Menna Talla Mamdouh Saoud

Subervised by Dr. Heba ElbehTeam Members7/5/2015DRS 2

What is DRS?Why is DRS?System Architecture & ComponentsDiabetes Diagnosis (using DM)Drug & Diet Recommendation (using SW)How DRS WorksDRS Tools & Techniques

Agenda7/5/2015DRS 3

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?

7/5/2015DRS 4

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?

7/5/2015DRS 5

3. DRS Architecture and Components. 7/5/2015DRS 6

4. Diabetes Diagnosis(Using Data Mining)7/5/2015DRS 7

Data Mining Data Mining Steps

7/5/2015DRS 8

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 Steps7/5/2015DRS 9

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

7/5/2015DRS 10

5. Drugs & Diet Recommendation(Using Semantic Web)7/5/2015DRS 11

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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Semantic Web Cake Layer.SW Knowledge Representation & Layers

Ontology7/5/2015DRS 13

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, axiomsare explicitly defined

Consensualknowledge

Abstract model of some phenomenain the world7/5/2015DRS 14

Ontology Main Components

Ontology (cont.)PersonCountry

Class (concept)

Animal

Individual (instance)

BelgiumParaguayChinaLatvia

ElvisHaiHolgerKylieS.Claus

Rudolph

Flipperarrow = relationshiplabel = Property

lives_inlives_inlives_in

has_pethas_pethas_pet7/5/2015DRS 15

Step by Step Building Ontology7/5/2015DRS 16

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 Ontologies7/5/2015DRS 17

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 Ontology7/5/2015DRS 18

Define classes hierarchy: define classes in taxonomic (subclass) hierarchy.

Anti Diabetic Drugs Ontology(cont.)

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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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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 Ontology7/5/2015DRS 21

Define classes hierarchy: define classes in taxonomic (subclass) hierarchy.

Patient Tests Ontology(cont.)

7/5/2015DRS 22

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 Ontology7/5/2015DRS 23

Define classes hierarchy: define classes in taxonomic (subclass) Diabetes Foods Ontology (cont.)

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Define Properties: define relationships . Data property: link individual to individual.Object property: link individual to literal.

Diabetes Foods Ontology (cont.)

7/5/2015DRS 25

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 Ontology7/5/2015DRS 26

Define classes hierarchy: define classes in taxonomic (subclass) hierarchy.Patient Information Ontology

7/5/2015DRS 27

SWRL Semantic Web Rule LanguageSWRL is a rule language for semantic webAll rules are expressed in terms of OWL concepts (classes, properties, individuals).Rule Example:

We use in writing the diabetes medication rules.

SWRL for Medication Rules7/5/2015DRS 28

We used the SWRL in DRS to represent the relationship between diabetics important tests and suitable drugs. Diabetics medication rules:

7/5/2015DRS 29SWRL for Medication Rules

6. How DRS Works?

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DRS Diabetes Diagnosis

Users

User Interface FormWeka API1.Get user information2. Load Model3. Classify User4. Return Result back to userClassifier Model- Stored in DRSLoading modelSubmitCheck resultPassing userdataModel Result 7/5/2015DRS 31

DRS Drug recommendation

Protg API7/5/2015DRS 32Jess EnginePatient inputRecommended Drug or patientDiabeticsUsers

SubmitBridge

Drugs Ontology

Patients Tests ontologySWRL Medication RulesLoading

DRS Diet Recommendation7/5/2015DRS 33

Diabetics Users

SQWRLQueriesFoods OntologyPatients Information OntologyProtg APIJess EngineRecommended Personal meal BridgeLoadingFavorite Foods& Personal Information

7/5/2015DRS 347. DRS Tools & Techniques

Thanks For Listening 7/5/2015DRS 35

Questions? 7/5/2015DRS 36