Abstract— Due to the progressing growth of temporal data,

data analysis and exploration become more and more difficult

task; particularly in the medical domain. For this reason, it

seems important to use Decision Support System (DSS) based

on the decisional tool Knowledge Discovery in Data (KDD) and

the visualization techniques for assisting user to get and

understand information. Our research is based on the visual

representations of an existing KDD based DSS for the fight

against nosocomial infections. In this paper, we are interested

in proposing a method, focusing on the user evaluation of this

system.

I. INTRODUCTION

The continuous increasing data volumes and the treatment of complex temporal databases leads to use decision support systems based on efficient decisional tools. Among these tools we can cite the Knowledge Discovery in Data allowing generating a set of patterns useful for decision making.

The information visualization techniques can be integrated in the KDD based DSS to improve decision making by taking into account the human judgment of the user.

Visual representations of a KDD based DSS are used to visualize data. These representations aim to help decision makers to better understand and interpret the information.

This research concerns visual representations of an existing KDD based DSS. This system is currently under use in the ICU of the Teaching hospital Habib Bourguiba in Sfax, Tunisia. It was developed to assist physicians to understand and prevent nosocomial infections.

The fight against nosocomial infections is considered a mature problem in an ICU. They are defined as infections that occur 48 hours after admission of a patient to the hospital. To verify the occurrence of these infections, the physician has to check the patient's state by following the data evolution over time. The visual KDD based DSS makes use of temporal visualization techniques for treating ICU patient temporal data.

According to our knowledge, there is no evaluation method of visual representations of a DSS based on the knowledge discovery in data on the one hand and the

temporal visualizations techniques on the other hand. In this context, our work aims to propose a user-centered methodology for evaluating visual representations of a KDD based DSS.

This paper is organized into 4 sections. In Section 2, the theoretical background concerning DSS, KDD and visualization is presented. In section 3, our methodology is proposed. In section 4, we discuss how we have applied the proposed approach. Our conclusions and perspectives end this paper.

II. THEORETICAL BACKGROUND

A. Decision Support Systems

The decision support is a broad field. This field allows decision makers to understand decisional situations, presenting possible solutions, justifying the optimal one and assessing the risks that may arise as a result of each solution [10].

Decision Support System DSS is an interactive system developed with the aim of solving decision problems and facilitating decision making. It is characterized by the interaction with the user through the Human-Computer interfaces, which is one of its components. The DSS takes into account the temporal aspect of its database and helps the decision maker to adapt to the change of environment by the evolution of the data in time.

The applicative context of the developed KDD based DSS is medical. It concerns the fight against nosocomial infections in the Intensive Care Unit of the Teaching hospital Habib Bourguiba in Sfax, Tunisia. The Database of this system is characterized by the large quantity of the temporal data. This temporal system is used with a decisional -support tool: KDD [8].

B. Knowledge Discovery from Data

The Knowledge Discovery in Data (KDD) is a decision-support tool aiming at discovering the connections between the fixed and temporal data elements for extracting new patterns useful for decision-making. As a definition; the KDD is an interactive and iterative process that extracts new, useful and valid knowledge from a mass of data [5]. This process proceeds according to a succession of stages: (1) problem definition, (2) data selection, (3) data cleaning and transformation, (4) data mining to discover patterns, (5) interpretation and evaluation of these patterns and (6) knowledge integration [7].

Approach for the evaluation of a KDD based DSS

visual representations

Awatef BRAHMI1, Hela LTIFI1 IEEE Member, and Mounir BEN AYED1,2 IEEE Member

1REGIM-Lab: REsearch Group on Intelligent Machines,National School of Engineers (ENIS),

University of Sfax, Tunisia 2Faculty of Sciences of Sfax, Computer Science and Communications Department, University of Sfax, Tunisia

brahmiawatef10@gmail.com; hela.ltifi@ieee.org; mounir.benayed@ieee.org

2014 Middle East Conference on Biomedical Engineering (MECBME)February 17-20, 2014, Hilton Hotel, Doha, Qatar

978-1-4799-4799-7/14/$31.00 ©2014 IEEE 338

As mentioned above the DSS and the KDD are interactive; the Human-Computer Interaction aspects must be integrated in their stages [8] [6]. For this reason the use of the visualization techniques can be used to increase the taking into account of the user [8]. The visualization techniques developed are temporal because of the temporal data treated by the KDD-based DSS in question of this research.

C. Temporal visualization techniques

The information visualization is the representation of information as interactive visual structures which purpose is to allow the user understand and interpret the information [8].

Visualization techniques of temporal data generate interactive graphics designed not only to provide temporal view of data to users, but also to help them explore and understand this information [1]. Each technique has a timeline for navigation in time. In the applicative context:

The perspective wall Technique [9] was used to give a global view of the patient fixed and temporal data.

The Star Representation Technique [4] was used to visualize taken antibiotics by the patient

The Tabular Representation Technique [4] to visualize the medical acts of each patient.

Superposed Representation Technique [4] to visualize the nosocomial infectious exams.

C. Motivation

The KDD-based DSS using temporal visualization techniques is currently under use by the ICU physicians since three years ago. The temporal ICU database contains now a great quantity of records (raw data and pattern discovered by the data mining techniques [9]).

An evaluation application of the Human-Computer interfaces was proposed by bahloul [3] and installed in the ICU to check the quality of the KDD-based DSS interfaces. However, this application does not take into account the visual representations generated by the temporal visualization techniques. This evaluation does not consider technical specificities of the visualization and the temporal data fields.

To overcome these insufficiencies, we propose an evaluation approach. It is the object of the following section.

III. PROPOSED EVALUATION METHODOLOGY

The evaluation is a field aiming to check the quality of a system Human-Computer Interfaces. To propose an approach it seems important to be based on a set of ergonomic criteria.

The system, question of our evaluation, uses visualization techniques of temporal data which makes its interfaces dynamic allowing assisting physicians and hygienists to predict the occurrence of nosocomial infections (NI) and decide the appropriate treatment to be applied against these infections.

The objective of our work is to propose an approach that takes into account the following concepts: Human-Computer Interaction, DSS situations, KDD, data visualization and

temporal aspects in order to reach an assessment of visual representations of a KDD-based DSS (Fig. 1).

Figure 1. Evaluation criteria

In fact, there are many models and methods to evaluate decision support system interfaces:

(1) the approach proposed for the evaluation of a KDD-based DSS ([3]),

(2) Another approach interested in the evaluation of visual data mining ([2] ),

(3) And there is also another evaluation work of visualization techniques of geography temporal data ([4]).

However none of these works focuses on the assessment of the visual representations of a KDD-based DSS whose purpose is to facilitate the processing of large amount of temporal data and help users predict the future and make the best decision.

Our proposal is to combine these different works using as a starting point the evaluation grid method and fuzzy logic proposed by Bahloul [3] to evaluate the KDD-based DSS. This method is based on two approaches:

(1) A User-centered approach: based on a questionnaire designed according to the needs of users. This questionnaire is carried out in the diagnosis of use (Table 1). The result is to make sure that the system meets the requirements.

(2) Approach based on the expert: it is carried out by an ergonomic expert or specialist in Human-Computer communication. This evaluation approach is performed via a grid developed using the questionnaire of the user-centered approach. The gird is filled by the domain expert according to criteria and themes used to characterize the system.

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TABLE I. GRID EVALUATION

N° Questions 3 2 1 0

KDD-based DSS evaluation

U Usability

1 Learning facility

2 Consistency (colors, names and abbreviations)

3 feedback

4 User help (explanatory document)

5 User Manuel: guide

6 Treatment and error correction

7 Use efficiency (tasks information, direct access)

Ut Utility

1 Adequacy of system to its needs

2 Facility of communication

3 Ease of understanding

4 Conviviality

5 Personalization

6 Flexibility

7 Pragmatism

Evaluation of visual representations of temporal data

V Visualization

1 Detecting missing or incorrect data

2 comprehensibility

3 speed

4 rendering multiplicity

5 User preference for multiple rendering

6 Detection of knowledge by graphics primitives

7 Using colors from graphics primitives

8 Compatibility and consistency of interactions

9 Cognitive task - informational density

10 Cognitive task - large data treatment

11 Cognitive task - data visualization

12 Basic task – zoom

13 Basic task - details on request

14 Basic task – filtering

15 Basic task – extraction

16 Basic task – Historical

17 Basic task – overview

18 Basic task – comparison

DT Temporal data

1 Navigation over time

2 Search a given data at a given time

3 Same data in different dates

4 Many data at the same time

For each of these criteria presented in Table 1, we give linguistic values using the technique of fuzzy logic. In fact, this technique follows three steps: (1) fuzzification to translate a numeric variable in a linguistic variable with a membership function defined in the interval [0,1]; (2) motor inference that allows to apply the rules of fuzzy logic and; (3) defuzzification to merge the different results into one [11].

We assign to each criterion presented above three linguistic variables as input variables and four linguistic variables as output (representing the evaluation result).

Usability (U): low, medium and high

Utility (Ut): low, medium and high

Visualization (V): unclear, medium and clear

Temporal data (Dt): difficult, medium and easy

And the output evaluation result E: low, medium, high and very high.

These criteria are expressed by the following curves that determine their membership degrees:

Figure 2. Membership degrees

After the fuzzification of the input and output variables, we must establish the rules linking inputs to outputs. Our system generates a base containing 81 “if-then” possible rules.

With each input variable there is a unique membership function associated. The membership functions associate a weighting factor (Wi) with values of each input and the effective rules. These weighting factors determine the degree of membership each active rule has. By computing the logical product of the membership weights for each active rule, a set of fuzzy output response levels are produced. All that remains is to defuzzify these output responses [Ross].

Our principle is to attribute to each issue of the grid one of the values: 3 (always), 2 (often), 1(sometimes), 0 (never) from which we determine the membership degree of each criterion μc (degree of membership of criteria c). Using this value, the Wi of each rule is calculated based on the formula:

Wi=μU*μUt* μV*μDt

Applying the weighted sum average:

E=

The conclusion of each fuzzy rule is the membership of the fuzzy output variable to the fuzzy class. Hence, we project the membership degree on the following curve to present the evaluation result.

Figure 3. Membership degree of the evaluation result

(2)

(1)

Σ Wi Xi

Σ Wi

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IV. EXAMPLE OF APPLICATION

In this section, we present the application of our method to a developed perspective wall interface (cf. fig 4).

Figure 4. Perspective wall presenting the distribution graph of acts carried

out by hospitalized patients at a chosen date [9]

Because of the lack of space, we cannot present the evaluation grid filled by the user, who is an expert in his/her domain (nosocomial infections).

Applying the appropriate rules and formulas (1 and 2), we found that the output E is equal to 0,63. By projecting this value on the curve, we find that evaluation is 84% high and 15% Very high (cf. Fig. 5).

Figure 5. evaluation result of the perspective wall interface

The evaluation of the visual representation in figure 4 has shown that physicians have, in general, appreciated using the temporal visualization technique: perspective wall.

V. DISCUSSION AND CONCLUSION

Evaluation provides the ability to verify and to improve the performance of a system. In this context, we have proposed a method for the evaluation of the visual representation generated by temporal visualization techniques. These techniques are developed in the framework of a KDD-based DSS.

Our proposition is based on an evaluation grid that takes into account the usability and utility dimensions, the visualization and the temporal aspects. To this grid, it is question to apply the fuzzy logic in order to generate the four criteria and evaluation result curves.

This method was applied to an existing DSS based on KDD and temporal visualization techniques. This system is under use in the ICU of the Teaching hospital Habib Bourguiba in Sfax, Tunisia. We have presented in this paper the evaluation of a visual representation generated by the perspective wall technique and has shown that physicians are satisfied by this representation.

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