Other joining methods

as shown in figure 3.6a and 3.6b.

Knowledge Base: The knowledge base is a collection of rules (cases) or other information structures derived from the human expert. Rules are typically structured as If/Then statements of the form:

IF <antecedent> THEN <consequent>

The antecedent is the condition that must be satisfied. When the antecedent is satisfied, the rule is triggered and is said to "fire". The consequent is the action that is performed when the

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rule fires (Danny, 2019). That is it contains essential information about the problem domain and is often represented as facts and rules (Samson and Lotfi, 2013).

(a)

(b)

Figure 3.6 CBR and Knowledge Base Components of the New System

Inference Engine: The inference engine is the main processing element of the new system expert Business Intelligence process. It chooses rules (similar cases) from the agenda (the case

Intelligence Process

Decision Making Process Problem Structuring

Intelligence Gathering Analysis & Review

Choice Phase

Inference Engine Knowledge-Base

Case Library

Case-Based Reasoning

CBR Alternative

Views Role

Knowledge Acquisition

Suggest &

Adviser Role

New Diseases < D >

Databases of Cases

(<disease.1> <soln.1> <result.1>

. . . matching

Prioritized list of similar matching cases

adaptation mechanism

adaptation solution

real-world evaluation

new case: <Disease> <Solution> <Result>

indexing

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library of knowledge base) to fire. If there are no marching rules (similar cases) on the agenda, the inference engine would obtain information from the user (medical expert such as the doctor) in order to add more rules to the agenda. It makes use of knowledge base (the case library database), in order to draw conclusions for situations (present cases). It is responsible for gathering the information from the user, by suggesting various similar treatment procedures for the present case and applying it wherever necessary. It seeks information and relationships from the knowledge base and to provide answers, predictions and suggestions the way a human expert would. It is the mechanism to derive and add new knowledge to and from the knowledge base.

Case Library: is where cases (rules) are stored and updated for future use. As well it is searched for suggestion purpose to solve new cases or situations.

The User Interface: This the method by which the expert system interacts with the user. It is also the development and maintenance avenue for the knowledge base. It carries out these functions through dialog boxes, command prompts, forms or other input methods. In the research, forms and dialog boxes was used. Figure 3.7 depicts the user interface.

Figure 3.7 User Interface of the New System

Data Source Layer: This is the data warehouse, which is defined as a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management’s decision making process. It is where the different and various heterogeneous sources of data either structured, unstructured or semi-structured, are stored and accessed either internally or externally such as via the web. Figure 3.8 shows the architectural model of the layer.

Query Input

USER INTERFACE LAYER

Intelligent Integrated Output

132 Figure 3.8 Data Source Layer of the New System 3.2.3. Advantages of the New System

In support of the new system justification, the following are the advantages;

1. The system ensures seamless transition from a practical workspace into a virtual business-oriented analysis world, expected by business users.

2. The system is based on hybrid architecture and also relies on elements such as system vocabulary and local ontology per each heterogeneous data source.

3. The system reduces syntax errors, structural and semantic heterogeneity and redundancy which leads to increased availability and degree of completeness.

4. With the layers of ontology-based and virtual data integration both implemented in the system, seamless transition and hiding of technical jargons from users was feasible.

5. With both approach to data integration in the system, reduced cost of processing, maintenance and risk in the system as well as increased availability will be feasible.

6. The system would assist medical researcher and practitioners’ experts to comprise up-to-date clinical and medical information into research process. This ensures real-time processing, analyzing and accessing of data across organizational boundaries.

7. The system would be intelligent, reliable, adaptive, flexible and agile. This would improve patients’ satisfaction with the use of business intelligence technology in addressing decision making in their treatment and care.

8. The system would be robust as it ensures data security as well as ensures managers at all levels in an organization (operational, tactical or strategic) make intelligent, correct and timely management decisions.

9. The ontology aspect sees to the reduction in latencies (data, analysis, and action latencies) in the decision process which allow users to take and make faster or fastest business decisions accessing current business data in its proper level of abstraction, thereby enhancing the ability of organization to adapt it as new necessities or in business changes.

10. The system delivers real-time performance analysis directly on the desktops of all levels of business managers which enable them become more knowledgeable and proactive.

. . .

DATA SOURCE LAYER

SQL WEB Files Legacy OTHERS

Existing Health Systems (EMR

& EHR)

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11. The system would bring about reduction in medical errors; improved patients’ safety, improved efficiency as well as it would consolidate data quality, integration and protect data which would help the health sector organizations stay proactive and be able to make decision with confidence.

12. It would enable analyzes of clinical data based on structured, semi-structured and unstructured information.

These justify the implementation of the new system to help health sector manage and control diseases and patients record.