05/01/2023DRS 1

Diabetes Recommender System)DRS)

(Using Semantic Web and Data Mining techniques)

05/01/2023DRS 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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1. What is DRS?2. Why is DRS?3. System Architecture & Components4. Diabetes Diagnosis (using DM)5. Drug & Diet Recommendation (using SW)6. How DRS Works7. DRS Tools & Techniques

Agenda

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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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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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3. DRS Architecture and Components.

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4. Diabetes Diagnosis(Using Data Mining)

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Data Mining

Data Mining Steps

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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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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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5. Drugs & Diet Recommendation

(Using Semantic Web)

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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

Ontology

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• 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 world

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Ontology Main Components

Ontology (cont.)

Person Country

Class (concept)

Animal

Individual (instance)

Belgium

Paraguay

ChinaLatvia

Elvis

Hai

Holger

Kylie

S.Claus

Rudolph

Flipper arrow = relationshiplabel = Property

lives_in

lives_in

lives_in

has_pet

has_pet

has_

pet

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Determine

ScopeData Gathering

&Consider Reuse

Enumerate TermsDefine

Classes & Class

Hierarchy

Define Properties

Check for

Anomalies

Step by Step Building Ontology

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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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• 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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• 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 Ontology

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

Patient Tests Ontology(cont.)

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• 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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• 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.)

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

Patient Information Ontology

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

Diabetics medication rules:

SWRL for Medication Rules

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6. How DRS Works?

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DRS Diabetes Diagnosis

Users

User Interface Form

Weka API1.Get user

information2. Load Model

3. Classify User

4. Return Result back

to user

Classifier Model

- Stored in DRSLoading

model

Submit

Check result

Passing userdata

Model Result

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DRS Drug recommendation

Protégé API

Jess Engine

Patient input

Recommended Drug or patient

DiabeticsUsers

Submit Bridge

Drugs Ontology

Patients Tests ontology

SWRL Medication Rules

Loading

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DRS Diet Recommendation

Diabetics Users

SQWRLQueries

Foods Ontology

Patients Information Ontology

Protégé API

Jess EngineRecommended

Personal meal

Bridge

LoadingFavorite Foods& Personal Information

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7. DRS Tools & Techniques

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Thanks For Listening

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Questions?