DSS an original one and no part of


DSS BASED ON DOCTOR PERSONAL RECOMMENDATIONS
ON A SPECIFIC DISEAS TO ENHANCE THE REVENUE

 

 

 

 

 

 

 

 

 

 

 

 

MUHAMMAD SHAHID

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(08-arid-538)

 

 

 

Barani
institute of information technology

Pir
Mehr Ali Shah

Arid Agriculture University Rawalpindi

Pakistan

2018

 

 

DSS BASED ON DOCTOR PERSONAL RECOMMENDATIONS ON
A SPECIFIC DISEAS TO ENHANCE THE REVENUE

 

by

 

 

MUHAMMAD SHAHID

(08-arid-538)

 

 

A
thesis submitted in partial fulfillment of

the
requirements for the degree of

 

 

 

Master
of Science

in

Computer
Science

 

 

 

 

 

Barani institute of information
technology

Pir Mehr Ali Shah

Arid Agriculture University Rawalpindi

Pakistan

2018

 

CERTIFICATE

 

 

I hereby undertake that this
research is an original one and no part of this thesis falls under plagiarism.
If found otherwise, at any stage, I will be responsible for the consequences.

Student
Name: Muhammad Shahid                           Signature:
_________________

Registration
No: 08-arid-538                                      Date: ____________________

 

            Certified
that the contents and form of thesis entitled “DSS Based on Doctor
Personal Recommendations on a Specific Disease to Enhance the Revenue” submitted by Muhammad Shahid has been found
satisfactory for the requirements of the MS (CS) degree.

Supervisor:
_____________________

                                                                                                   (Mr.
Muhammad Asif)

 

Member:
______________________

                                                                                                   (Dr.
Umair Abdullah)

 

Member:
______________________

                                                          
                                (Dr. Mohammad Jamil Sawar)

Date of Viva Voce:
________________          External Examiner: _________________

Director: _______________________________________

Director Advanced
Studies: ________________________

 

DEDICATION

 

I
dedicate this thesis to my parents and my teachers whose encouragement is
always a source of motivation and determination for me. Their support and
prayers enabled me to complete my work successfully and they are credited for
all my achievements. I am thankful to my friends for their support which always
contributed to my work.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                         

 

CONTENTS

 

Page

List of
Abriviations                                                                                                     ix

List of Tables                                                                                                              x

List of Figures                                                                                                             xi

Acknowledgements                                                                                                    xii

Abstract                                                                                                                      xiii

1    INTRODUCTION                                                                                     1

      1.1       BACKGROUND                                                                                     1

      1.2       PROBLEM STATEMENT                                                                      1

      1.3       SCOPE                                                                                                     1

      1.4       AIMS AND OBJECTIVE                                                                       2

      1.5       THESIS STRUCTURE                                                                            2

2    REVIEW OF LITERATURE                                                                                 5

      2.1       BACKGROUND                                                                                     5

      2.2       TECHNIQUE                                                                                           7

      2.3       DOMAIN                                                                                                 10

3    A PROPOSED DSS FOR DIABETES PREDICTION                               14

      3.1       THE OVERVIEW OF PROPOSED DSS                                               14

      3.2       CONCEPTUAL DIAGRAM OF PROPOSED DSS                              15

      3.3       DETAILED DIAGRAM OF PROPOSED DSS                                                16

      3.4       PROPOSED DSS DESIGN                                                              17

 

4    RESULTS AND DISCUSSION                                                                              19

      4.1       EXPERIMENT                                                                                        19

4.2        PROPOSED DSS TRAINING /
LEARNING                                       20

4.3     
 PROPOSED DSS PREDICTION                                                                       21

      4.4       PROPOSED DSS OUTPUT                                                                    21
4.5  PREDICTION WITH PROPOSED DSS                                                 22

      CONCLUSION AND FUTURE WORK                                                        23     SUMMARY                                                                                                   23      LITERATURE CITED                                                                                       24

 

 

 

 

 

 

LIST OF ABBREVIATIONS

DSS                 Decision
Support System

BIIT                Barani
Institute of Information Technology

EHR                Electronic
Heath Records

PMS                 Physician
Management System

PHR                Patient
Health Records

AI                    Artificial
Intelligence

SVM               Support
Vector Machine

SMO               Sequential
Minimal Optimization

KNN               K-nearest
Neighbors

ANN               Artificial
Neural Network

MLP                Multilayer
Perceptron

PIDD              Pima
Indians Diabetes Dataset

ML                  Machine
Learning

 

 

 

 

 

 

 

 

 

 

 

LIST OF TABLES

 

Table No.                                                                                                                   Page

4.1       Summary of Experiments                                                                               19

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LIST
OF FIGURES

 

Figure No.                                                                                                                  Page

3.1       System Architectural Diagram                                                                        14

3.2       Conceptual Diagram of Proposed DSS                                                          15

3.3       Detailed
Diagram of Proposed DSS                                                               16

 

3.4       Proposed DSS Inteface                                                                                  17

4.1       Rules Snapshow                                                                                              20

4.2
      Proposed DSS Training/Learning
Interface                                                    21

4.3
      Proposed DSS Prediction Interface                                                                21

4.4
      Proposed DSS Output                                                                                                22

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ACKNOWLEDGEMENTS

 

Immense
gratitude to Allah Almighty, the Most Beneficent and The Most Merciful who
blessed me with the desire to seek knowledge and to explore some of the many
aspects of His creation.

I would like to express my sincere
gratitude to my supervisor Mr. Muhammad
Asif for the continuous support for my research. I am thankful to him for
his patience, motivation, enthusiasm, and immense knowledge. His guidance
helped me in carrying out research work and writing this thesis. I attribute
the completion of my degree to his encouragement and effort and without him
this thesis would have not been possible. One simply could not wish for a
better or gracious supervisor.

I would like to thank members of my
supervisory committee; Dr. Mohammad
Jamil Sawar and Dr. Umair Abdullah for their support, invaluable advice and
guidance.

I am deeply grateful to my father Mr.
Muhammad Afzal and my mother
for immense support and high level of motivation throughout my study career. I
would like to thank all BIIT staff for their cooperation.

I would love to express my sincere
thanks to my colleagues and friends who have always been a real source of
motivation for me. Last but not the least, I would like to thank my family
especially my loving parents, my sisters, brothers, my wife and my children for
supporting me and encouraging me throughout my life.

 

 

ABSTRACT

 

With
the rapid growth of Health Care Information Systems, there has been much
advancement at different levels e.g. clinically, healthcare providers oriented
and patient specific. DSS (Decision Support Systems) are based on historical
data in terms of objectives reality, so success rates are very high in these
types of systems.  Historical data can be
used to generate useful patterns for decision making. Healthcare support systems like recommender systems play a vital role in different
aspects of in-clinic and out-clinic patient care.

DSS assist
its users in different aspect; an accurate DSS may facilitate its user in prediction
more accurately. In healthcare IT systems DSS help clinical staff more
intelligently on the base of knowledge extracted from historical data to
provide better care of patient. DSS comprises of multiple tools to produce a
decision in the healthcare system e.g. EHR (Electronic Health Records). These
tools generates different random reminders and alerts that provide both patient
and clinicians timely intimation about suffering diseases. DSS has numerous
benefits that includes, assist a care provider for better care of patient,
future prediction of patient suffering, producing precaution and quality care
that leads to patient satisfaction. DSS works jointly with EHR, PMS and PHR to
incorporate timely and quickly through systematic alerts and guidelines. DSS may
work as a standalone system in any health facility to corporate clinician’s
staff.

In
this research, a decision support system is proposed to predict a patient orientation
towards a specific disease that is diabetes. In the prediction two standard
techniques are used. First one is C4.5; a decision tree algorithm known as most
appropriate predictor in supervised learning and second one is support vector
machine (SVM). By implementing two famous standard predictors of data mining,
we developed a prototype of DSS to facilitate the clinicians, doctors and
physicians to estimate the diabetes orientation in a patient through biological
readings.

This
proposed DSS will be integrated with standard HealthCare Systems e.g. in EHR
(Electronic Health Records), PMS (Physician Management System), PHR (Patient
Health Records). Users of this system include Providers, Physicians or any
other prescriber who is using a standard healthcare system in a practice or
clinic. By training through a standard diabetes data set, generation of new prediction
of patient diabetes is the core research objective of this research. This can
directly or indirectly increase the rating of HealthCare Systems and provider
satisfaction over HealthCare system. Overall this system is built on historical
data to assist its users and for prediction of diabetes rather found positive
or negative. 

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