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Download the complete Computer science topic and material (chapter 1-5) titled DATA PRIVACY PRESERVING MODEL FOR HEALTH INFORMATION SYSTEM here on PROJECTS.ng. See below for the abstract, table of contents, list of figures, list of tables, list of appendices, list of abbreviations and chapter one. Click the DOWNLOAD NOW button to get the complete project work instantly.

 

PROJECT TOPIC AND MATERIAL ON DATA PRIVACY PRESERVING MODEL FOR HEALTH INFORMATION SYSTEM

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  • Name: DATA PRIVACY PRESERVING MODEL FOR HEALTH INFORMATION SYSTEM
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ABSTRACT

Information systems are employed by organizations for the collection, filtering, processing, creation and distribution of data. In healthcare delivery, patients are required to share information with certain categories of health personnel to facilitate correct diagnosis and to determine appropriate treatment. There have been cases of unauthorized access to patient information by health personnel. Some of these personnel eventually cause great harm to the patient by divulging sensitive information. The existing Data Privacy Preservation (DPP) models are designed for Clinical Decision Support Systems with inadequate information available for DPP in Health Information Systems (HIS) in Nigeria. This research, therefore focused on the development of a model for Data Privacy Preservation (DPP) in HIS to address this inadequacy.

A model for DPP in HIS was developed using the iterative design technique. The model developed comprises a local database that contains the health information of patients, the Random Forest Decision Tree (RFDT) algorithm, an attribute blocking module that employs the RFDT algorithm, an attribute unblocking module which also uses the RFDT algorithm and a module for the computation of time elapsed in unblocking attributes. Mandatory Role-based Access Control was used to restrict the access health professionals have to patient data; each category of health worker can only view the attribute(s) needed for them to provide the service required to fulfill their role. An application based on the RFDT algorithm, was developed to instantiate the model following the Waterfall Software Development Life Cycle. Netbeans Integrated Development Environment, MySQL server, Java Development Kit 8, Scene builder 2.0, and Navicat 8 query editor constitute the programming environment. The application was evaluated against the machine learning approach to DPP that employed the classification technique, by comparing its efficiency with the Waikato Environment for Knowledge Analysis (WEKA) version 3.8 software in ensuring DPP using the RFDT algorithm.

 

The model developed in this study provides a generic framework for DPP in HIS that reveals the necessary components. This model provides a template that could be adapted for use in studies on DPP in HIS. The application provides the health personnel with Graphical User Interfaces that depict the professional’s access to the patient database while restricting access to attributes not allowed for such category of health workers. The use of the RFDT algorithm in WEKA for DPP gave an efficiency of 73.77% while the approach that employed the application gave an efficiency of 78.32%.

The model presented in this study wouldhelp preserve sensitive patient data from being accessed by health workers who are not authorized to do so. The study showed that the application is more efficient than the WEKA software in ensuring DPP using the RFDT algorithm.The DPP model proposed in this study could also be employed in other domains outside the health sector to curb the challenges resulting from weak DPP.

Keywords:     Health Information System, Machine Learning, Data Privacy Preservation Model, Software Development Life Cycle, Random Forest Decision Tree

Word Count: 463

TABLE OF CONTENTS

TABLE OF CONTENTS

Content Page

Title Page                                                                                                                       i

Certification    ii

Dedication                  iii

Acknowledgements                                                                                             iv

Abstract           v

Table of Contents   vi

List of Tables                                                                                                             ix

List of Figures                                   x

CHAPTER ONE: INTRODUCTION

1.1 Background to the Study                        1

1.2 Statement of the Problem                3

1.3 Objective of the Study                             4

1.5 Justification for the Study                              5

1.6 Scope of the Study                        6

1.7 Operational Definition of Terms                      6

CHAPTER TWO: REVIEW OF LITERATURE

2.0 Introduction                       8

2.1 Schizophrenia                                      8

2.1.1 Symptoms of Schizophrenia                          9

2.1.2 Factors that cause Schizophrenia                      10

2.2 Clinical Decision Support Systems (CDSS)11

2.2.1 Types of Clinical Decision Support Systems (CDSS)                       11

2.2.1.1 Knowledge Based CDSS                               11

2.2.1.2  Non Knowledge Based CDSS                                   13

2.2.2 Modern Trends of Implementing Clinical Decision Support Systems     14

2.2.2.1 Statistical Method                             14

2.2.2.2 Hybrid Systems                            15

2.3 Data Privacy                                       15

2.3.1 Data Privacy Preserving Methods                                     16

2.3.1.1 Privacy Preserving Data Mining                              16

2.3.1.2 Privacy Preserving Data Mining Tasks and Algorithms                    17

Content                                                                                                                                           Page

2.3.1.3 Identification, Authentication and Authorization                    22

2.4 Role Based Access Control                                     24

2.5 Review of Closely Related Works                                      25

2.5.1 Cryptograhic Approach to DPP                             25

2.5.2 Machine Learning Approach to DPP                         27

2.5.3 Data Privacy Preservation Models                  28

CHAPTER THREE: METHODOLOGY

3.0 Introduction                        32

3.1 Research Design                                       32

3.1.1 Design of Experiment                     32

3.1.2 Variable Selection                            33

3.2 Research Methods                        33

3.3 Machine Learning Model Design – Algorithms                                38

3.4 Proposed Model for the Preservation of Patient Data Privacy                             38

3.4.1 Design of the Application                                  40

CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION OF FINDINGS

4.0 Introduction                    44

4.1 Blocking of Attributes across the Different Health Professional Categories Using WEKA                 45

4.2 Blocking of Attributes across the Different Health Professional Categories Using the

Application (Schizoapp)                               60

4.3 Unblocking of Attributes across the Different Health Professional Categories Using WEKA            65

4.4 Unblocking of Attributes across the Different Health Professional Categories Using the Application (Schizoapp)                                  80

CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION

5.0 Introduction                                        92

5.1 Summary                                      92

5.2 Conclusion                                       93

5.3 Recommendations                                      93

5.4 Suggestions for Further Studies                  93

5.5 Contribution to Knowledge                 94

5.6 Ethical Consideration                     94

Content                              Page

5.7 Post Research Benefits                                                                                                               95

References                    97

APPENDIX                 103

 

LIST OF TABLES

Table                                                                                                                                                Page

3.1 Description of Variables                            33

3.2 Healthcare Professionals and their access levels to the system database      37

3.3 Healthcare Professionals and the attributes they are not allowed to view             37

 

LIST OF FIGURES

Figure Page

2.1 Different methodological branches of Clinical Decision Support Systems              14

 

2.2 Two modern trends of implementing Clinical Decision Support Systems            15

 

2.3 Resource Access Stages                 24

 

2.4 Architecture of a Cloud-Based eHealth Model for Privacy Preserving Data Integration             28

 

2.5 Architecture of a Data Privacy Preserving Model for a Clinical Decision Support System                   29

 

2.6 Architecture of a Privacy Preserving Data Classification Model                         30

 

3.1 Clinical Decision Support System Model depicting internal modules and external Health

Information System                                                                38

 

3.2 Proposed Model for the Preservation of Patient Data Privacy                     39

 

3.3 Clinical Decision Support System Model depicting internal modules and Data Privacy

Preserving Section of external Health Information System   40

 

3.4Architectural Diagram for Schizoapp41

 

3.5Flowchart for Schizoapp                                                                             42

 

3.6 The Use Case diagram showing users’ interaction with the database of the HIS                        43

4.1Pre processing of data before blocking attribute from the view of the Psychologist using the

WEKA software                       45

 

4.2The use of the Random Forest Algorithm to block the third person auditory hallucination

attribute using WEKA               46

 

4.3 List of Attributes showing the third person auditory hallucination attribute           47

 

4.4 WEKA Interface showing the Pre processing of data before blocking attributes from the Nurse      48

 

4.5Use of Random Tree Algorithm to block the third person auditory hallucination attribute          49

 

4.6 List of Attributes showing the third person auditory hallucination attribute  50

 

4.7 Use of Random Tree Algorithm to block the delusions of control attribute 51

Figure                                                                                                                                              Page

4.8 List of attributes showing that the third person auditory hallucination and delusions of

control attributes have been blocked              52

 

4.9 WEKA Interface showing the Pre processing of data before blocking attributes from the

view of the Social Worker           53

 

4.10 Use of Decision Stump Algorithm to block the third person auditory hallucination attribute  54

 

4.11 List of Attributes showing that the third person auditory hallucination attribute              55

 

4.12 Use of Decision Stump Algorithm to block the delusions of control attribute       56

 

4.13 List of attributes showing that the third person auditory hallucination and delusions

of control attributes have been blocked                           57

 

4.14 Use of Decision Stump Algorithm to block the Thought Echo,Insertion

or Withdrawal attribute                      58

 

4.15 List of Attributes showing that the third person auditory hallucination, delusions of

control and Thought Echo, Insertion or Withdrawal attributes have been blocked   59

 

4.16 Authentication Page of the Application                  60

 

4.17 Page showing that the user is authorized to view patient records            61

 

4.18 Doctor’s view of patient data with none of the attributes of interest blocked            62

 

4.19 Psychologist’s view of patient data with one of the attributes of interest which is third

person auditory hallucination blocked                 63

 

4.20 Nurse’s view of patient data with two of the attributes of interest which are third person

auditory hallucination and Delusions of Control blocked      63

 

4.21 Social worker’s view of patient data with all three attributes (third person auditory

hallucination, delusions of control and Thought echo, insertion or withdrawal) are blocked       64

 

4.22 WEKA Interface showing the Pre processing of data before unblocking attributes for the

view of the Psychologist          65

 

4.23 Use of Random Forest Algorithm to unblock the third person auditory hallucination

attribute            66

 

4.24 List of Attributes showing the third person auditory hallucination attribute                         67

 

4.25  WEKA Interface showing the Pre processing of data before unblocking attribute for view of the Nurse         68

Figure                                                                                                                                              Page

 

4.26 Use of Random Tree Algorithm to unblock the third person auditory hallucination attribute for the view of the Nurse      68

 

4.27 List of Attributes showing that the third person auditory hallucination and delusions of control attributes have been unblocked for the view of the Nurse                 70

 

4.28 Use of Random Tree Algorithm to unblock the delusions of control attribute for the

view of the Nurse                            71

 

4.29 List of Attributes showing that the third person auditory hallucination and delusions

of control attributes have been unblocked for the view of the Nurse              72

 

4.30 WEKA Interface showing the Pre processing of data before unblocking attribute for view of the Social Worker 73

 

4.31 Use of Decision Stump Algorithm to unblock the Thought Echo, Insertion or Withdrawal attribute for the view of the Social Worker Using WEKA          74

 

4.32 List of Attributes showing that the Thought Echo, Insertion or Withdrawal attribute has been unblocked for the view of the Social Worker                75

 

4.33Use of Decision Stump Algorithm to unblock the Delusions of Control attribute for the view

of the Social Worker using WEKA76

 

4.34 List of Attributes showing that the Delusions of Control attribute has been unblocked for the

view of the Social Worker               77

 

4.35 Use of Decision Stump Algorithm to unblock the Third Person Auditory Hallucination

attribute for the view of the Social Worker 78

 

4.36 List of Attributes showing that the Thought Echo, Insertion or Withdrawal, Delusions of Control and Third Person Auditory Hallucination attributes have been unblocked for the

view  of the Social Worker                    79

 

4.37 Administrator’s Authentication Page                       80

 

4.38 Page showing that the Administrator is authorized to unblock patient records                   81

 

4.39 The Nurse’s Page showing that Third Person Auditory Hallucination and Thought Echo,

Insertion or Withdrawal attributes are blocked                      82

 

4.40 The Nurse’s Page showing that Third Person Auditory Hallucination attribute has been unblocked by the Administrator                     83

 

Figure                                                                                                                                              Page

4.41 The Nurse’s Page showing that the Thought Echo, Insertion or Withdrawal attribute has been unblocked by the Administrator                          84

 

4.42 The Social Worker’s Page showing that Third Person Auditory Hallucination, Thought

Echo, Insertion or Withdrawal and Delusions of Control attributes are blocked             85

 

4.43 The Social Worker’s Page showing that the Third Person Auditory Hallucination

attribute has been unblocked by the Administrator                86

 

4.44 The Social Worker’s Page showing that the Thought Echo, Insertion or Withdrawal

attribute has been unblocked by the Administrator   87

 

4.45 The Social Worker’s Page showing that the Delusions of control attribute has been

unblocked by the Administrator                                88

 

4.46 The Psychologist’s Page showing that Third Person Auditory Hallucination, attribute is

blocked89

 

4.47The Psychologist’s Page showing that the Third Person Auditory Hallucination attribute has

….. been unblocked by the Administrator                                 90

 

4.48 Administrators’s Page showing the time elapsed in unblocking attributes across each

category of Healthcare Professional as well as the total time elapsed in unblocking

all previously blocked attributes using both WEKA and the Application       91

 

4.49 Administrators’s Page showing the efficiency of the WEKA approach and the Application approach to data Privacy Preserving with information on the more accurate one after testing

both approaches with the same data                                  91

 

  

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Health Information Systems(HIS) provide the bedrock for decision-making and has four key

functions: data generation, compilation, analysis and synthesis, and communication and use. The HIS gathers data from the health sector and other relevant sectors, analyzes the data and ensures their overall relevance, quality, and timeliness, and converts data into information for health-related decision-making.In addition to being essential for monitoring and evaluation, the information system also provides early warning capability,supports patient and health facility management, facilitate planning, supports and stimulates research, permits health situation and trends analysis, supports global reporting, and under pins communication of health challenges to diverse users (WHO, 2009).

 

To improve the quality of medical care around the globe,efforts are being made to increase the practice of evidence-based medicine through the use of an HIS called Clinical Decision Support Systems (CDSS). Clinical Decision Support provides clinicians, patients, or caregivers with clinical knowledge and patient-specific information to help them reach decisions that enhance patient care (Osheroff, Teich & Middleton, 2011). The patient’s information is matched to a clinical knowledge base, and patient-specific appraisals are then communicated effectively at appropriate times during patient care. Some CDSS include forms and templates for entering and documenting patient information, and alerts, reminders, and order sets for providing suggestions and other support. The use of CDSS comes with many potential benefits. Importantly, CDSS can increase adherence to evidence-based medical knowledge and can reduce unnecessary variation in clinical practice. CDSS can also assist with information management to support the physicians’ decision making abilities, reduce their mental workload, and improve clinical workflows (Karsh et al., 2010). When well designed and implemented, CDSS have prospects that can improve health care quality, and also to increase efficiency and reduce health care costs (Berner, 2010).

 

Despite the promise of CDSS, there are several barriers that can hinder their development and implementation. Till date, Medical knowledge base is incomplete in part because of insufficient clinical evidence (Englander & Carraccio, 2014). Moreover, methodologies are still being designed to convert the knowledge base into computable code, and interventions for conveying the knowledge to clinicians in a way they can easily usein practice are in the nascent stages of development. Low clinician demand for Clinical Decision Support is another encumbrance to broader CDSS adoption. Clinicians’ lack of motivation to use CDSS appears to be related to usability issues with the Clinical Decision Support intervention, its lack of integration into the clinical workflow, concerns about autonomy, and the legal and ethical implications of adhering to or overriding recommendations made by the CDSS (Berner, 2010). In addition, in many cases, acceptance and use of CDSS are hinged uponthe adoption of electronic medical records (EMRs), because EMRs can include Clinical Decision Support applications as part of Computerized Provider Order Entry (CPOE) and electronic prescribing systems.

One of the five recommendations made for CDSS in connection with the practice of Evidence-based Medicine was to “develop maintainable technical and methodological foundations for computer-based decision support” (Sim, Gorman & Greenes, 2011). Also, the medical domain is “characterized by much judgmental knowledge”. Consequently, a CDSS that can provide suggestive knowledge representations based on data sets with patient attributes that are synonymous with the attributes of the patient in context is valuable to a medical practitioner. Invariably, there are situations where the number of local samples to draw conclusions from, is none or few. Several current challenges have not been sufficiently addressed during the development of CDSS. From latest research, the lists of challenges include: improvement of the human-computer interface, dissemination of best practices in CDSS design, development, and implementation, creation of an architecture for sharing executable CDSS modules and services, combination of recommendations for patients with co-morbidities,summary of patient-level information, prioritization and filtering of recommendations to the user, prioritization of CDSS content development and implementation, creation of Internet-accessible clinical decision support repositories, usage of free text information to drive clinical decision support, and mining of huge clinical databases to create new CDSS (Kumar&Prabha, 2016).

Psychiatry is one branch of medicine that urgently needs HIS owing to the fact that there are relatively few specialists in that area of medicine (Saha,Chant, Welham, & McGrath, 2015). According to the National Alliance on Mental Illness, mental illnesses are medical conditions that disrupt a person’s clear thinking, feeling, mood, ability to relate to others, decision making ability and daily functioning (NAMI, 2011). Mental illnesses include schizophrenia, depression, bipolar disorder, obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), borderline personality disorder, anxiety disorder and others. However, schizophrenia involves a relatively higher display of psychotic symptoms than most other mental illnesses (Amin, Agarwal & Beg, 2013).

Schizophrenia is a chronic and debilitating illness characterized by perturbations in cognition, affect and behavior, all of which have a bizarre aspect (Lehman et al., 2010). Due to the fact that schizophrenia is a stigmatized illness it is important for schizophrenic patients’ data to be kept with a high degree of secrecy so as to avoid sensitive patient data being divulged. It is therefore expedient that in Clinical Decision Support Systems that contain data of Schizophrenic patients, access to patient data by the healthcare givers be restricted based on their roles in the hospital. This can be achieved by employing access control. The Mandatory Role-Based Access Control is a type of access control and can be employed for such a study as this. To boost the security of a Health Information System (HIS) through data privacy preservation, this study proposes a model for implementing data privacy preservation in a HIS. This model would help boost the security of the HIS in question through the restriction of access of users to its database.

This study proposes a Data Privacy Preservation (DPP) model for HIS. In order to guarantee the secrecy of sensitive patient data domiciled in a HIS, the study involved the development of an application named Schizoapp which was used to instantiate the proposed DPP model and effected data privacy by blocking attributes on a patient database based on the Mandatory Role-Based Access Control (MAC) model which is used to assign access rights to different categories of health professionals based on their role in the hospital. The study also compared the use of the application (Schizoapp) developed in this study for data privacy preservation with the machine learning approach to data privacy preservation which employed the Random Forest Decision Tree algorithm embedded in the WEKA software.

1.2 Statement of the Problem

In healthcare delivery, patients are required to share information with certain categories of health personnel to facilitate correct diagnosis and to determine appropriate treatment. However, patients would most of the time prefer their sensitive information to be kept secret particularly from persons that need not have access to such information especially in cases of health problems such as schizophrenia as the disclosure of such private information may lead to social stigma and discrimination. There have been cases where health personnel who by virtue of their role ought not to have access to certain patient information gained access to such information. Some of these health personnel cause harm to the patient in question by divulging such details to other individuals thereby jeopardizing the patient’s health. Hence, the healthcare system becomes the worse for it as a number of patients may relapse to worse states they already improved from and the retrogression in the patients’ health status will in the long run take a toll on the healthcare system.

The existing Data Privacy Preservation (DPP) models are designed for Clinical Decision Support Systems with inadequate information available for DPP in Health Information Systems (HIS) in Nigeria. This research, therefore focused on the development of a model for Data Privacy Preservation (DPP) in HIS to address this inadequacy.

1.3 Objective of the Study

The main objective of this study is to propose and implement a DPP model for HIS.

The specific objectives are to:

  1. propose a model for DPPin HIS;
  2. develop a DPP application to instantiate the model for DPPin HIS;
  3. implement DPP in a HISusing the application developed in ii and
  4. evaluate the prototype application developed for its efficiency

1.4 Methodology Overview

  1. Major existing DPP models were reviewed and grouped into three clusters from which the most recent model in each cluster was selected. The three models chosen from the clusters are (A Cloud-Based eHealth Model for Privacy Preserving Data Integration by Dubovitskaya, Urovi, Vasirani, Aberer and Schumacher(2015); A Data Privacy Preserving Model for a Clinical Decision Support System by Deshmukh, Tijare and Sawalkar(2016) and A Privacy Preserving Data Classification Model by Desale and Javheri(2016)). In the course of reviewing the models, the flaws in each of the models were highlighted. Taking into consideration the flaws identified in the models, a model for data privacy preservation in an HIS was proposed. The proposed model consists of:
  2. a local database that contains the health information of patients
  3. the Random Forest Decision Tree (RFDT) algorithm
  • an attribute blocking module that employs the RFDT algorithm
  1. an attribute unblocking module which also uses the RFDT algorithm and
  2. a module for the computation of time elapsed in unblocking attributes.
  3. An application for data privacy preserving in a Health Information System named Schizoapp was built using the Waterfall Software Development Life Cycle Model and the following tools were employed:
  4. Netbeans Integrated Development Environment (IDE)
  5. MySQL server
  • Java Development Kit (JDK) 8
  1. Scenebuilder 2.0
  2. Navicat 8 query editor
  3. JavaFX
  4. Using the Mandatory Role-Based Access Control, access to patient data as regards the three sensitive attributes of the eleven attributes in the dataset by the four categories of healthcare professionals considered in this study (doctors, nurses, psychologists and social workers) is restricted such that each category is only allowed to view the attribute(s) needed for them to provide the service needed by the patient.

The attribute(s) of the three which each category is not allowed to see is blocked in the database so that the health worker in that category can only see the other attributes in order to ensure the preservation of patient data privacy. Graphical User interfaces were generated to depict the view of each healthcare professional to patient data.

  1. The machine learning approach to data privacy that involved the use of the Random Forest Decision Tree algorithm in the WEKA software for DPP was compared with the application based approach which employed the proposed DPP application. Both approaches were evaluated for efficiency based on the quantum of time taken to unblock the attributes. Hence, the better approach for data privacy preservation is the one which took a longer time for the blocked attributes to be unblocked.

1.5 Justification for the Study

This study will bring to the fore, the need for Psychiatric hospitals in Nigeria to adopt Electronic Health Records for patient data rather than the present method used by most of them which employs paper records for patient data. The study when implemented by the Psychiatric hospitals in Nigeria will help mitigate the intrusion of patient data privacy by restricting access to patient data only to the persons that are eligible to view such information by virtue of their role as healthcare professionals needed to keep the patient in a state of good health. By implementing the data privacy preserving model that was proposed in this study, the menace of schizophrenic patients in Nigeria being stigmatized based on their schizophrenic status would be mitigated to a reasonable degree.

1.6 Scope of the Study

The study focused on preserving the privacy of data belonging to some schizophrenic patients and some of the people that have been interrogated by psychiatrists at one time or the other to ascertain if they were schizophrenic or not. For the purpose of this study, two Psychiatric hospitals were visited to gather the data required for the study. The two hospitals were Federal Neuropsychiatric Hospital, Yaba, Lagos and Neuropsychiatric Hospital, Aro, Abeokuta. Two hundred and sixty three anonymous records of persons that have earlier visited Federal Neuropsychiatric Hospital, Yaba on account of showing symptoms suggestive of schizophrenia and Two hundred and forty eight anonymous records of persons that have visited Neuropsychiatric Hospital, Aro, Abeokuta earlier on account of being linked with schizophrenic symptom(s) were gotten, giving a total of five hundred and eleven records, which were used for the research. The study used five hundred and eleven records due to the fact that this was the number of records both Psychiatric Hospitals used in this study were willing to release.

1.7 Operational Definition of Terms

Health Information System:This refers to any system that captures, stores, manages or transmits information related to the health of individuals or the activities of organizations that work within the health sector.

Model: This is a representation of an idea, an object or even a process or a system that is used to describe and explain phenomena that cannot be experienced directly.

Data Privacy: This deals with the ability an organization has to determine what data in a Health Information System can be shared with health personnel.

Mandatory Access Control: This refers to a type of access control by which the operating system constrains the ability of a subject or initiator to access or generally perform some sort of operation on an object or target.

Role Based Access Control: This is a policy neutral access control mechanism defined around roles and privileges used to restrict system access to authorized users.

Decision Tree is a flow-chart-like structure, where each internal (non-leaf) node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label.

Random Forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Machine Learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.

 

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