Mobile Agents using Data mining for Diagnosis Support in ... - CiteSeerX

Mobile Agents using Data mining for Diagnosis Support in Ubiquitous Healthcare Romeo Mark A. Mateo, Louie F. Cervantes, Hae-Kwon Yang and Jaewan Lee School of Electronic and Information Engineering, Kunsan National University 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea {rmmateo, lfcervantes, hkyang, jwlee}@kunsan.ac.kr

Abstract. Recent research topics in healthcare including intelligent decision support services, expert medical services and autonomous management are based on multi-agent systems. The cooperation of these software agents provides efficient monitoring, analyzing, and managing the data of patient where abnormal patterns are detected to have an advance treatment and prevent loss of life. In this paper, a framework for ubiquitous healthcare based on multi-agent is presented. This paper proposes a mobile agent for diagnosis support in ubiquitous healthcare. The expert mobile agent (EMA) classifies the data of patient by using neuro-fuzzy algorithm for consultation report. A pre-processing method based on the profile of an expert is used to filter the data from the history of patient. Result of neuro-fuzzy from cross-validation test shows a high accurate classification in data compared to other highly accurate classifiers.

1

Introduction

Agent-based healthcare system addresses the importance of intelligent programs to substitute the real person’s functions in healthcare services and management. This technique benefits most of individuals through their decision making and automation of their tasks. Most of these implementations are used for decision support system [1]. The use of agent-based intelligent support systems is important in medical industries because it allows doctors and nurses gather quick information. This information is processed in various ways to assist with making diagnosis and treatment decision. Also, because software agents deals with distributed systems, it assist in diversity of storing and retrieving medical records, analysis of real-time data gathered and other necessary information retrieval in distributed environment. Techniques and algorithms are integrated to the agent-based healthcare system for medical diagnosis. Neural network is a common technique for medical diagnosis [2, 3]. Successful application examples show that neural diagnostic systems are better than human diagnostic capabilities. Moreover, neural network are used to analyze medical images [4, 5]. These research articles survey various approaches and techniques to improved diagnosis in medical images, including mammography, ultrasound and mag* This work is supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2006-521-D00372).

netic resonance imaging. Neural network-based agents are used for discovering rules in medical database [6]. Medical databases, which consist of patient histories, specialist's conclusions, laboratory results, etc., are typically distributed set of semistructured data, and because agent technology is well-suited approach to develop the medicine decision supporting systems, the integration of neural network in the agent is necessary. Medical databases are dynamically changed because the structures of features characterizing the diseases are continuously updated. The features of diseases depend on tools and technologies those doctors and specialists currently use to diagnose and treat the patients. Even though the integration of neural networks within agents is well-researched, it still needs intensive research on using hybrid systems [20] because of the vast changes of information and the classical methods may not solve the problem of classification. In this paper, we propose an expert mobile agent using data mining to support the diagnosis of the patient in ubiquitous healthcare. Moreover, a framework of ubiquitous healthcare based on multi-agent is presented. The framework supports the mobility of the mobile agent which executes classification algorithm to the data of patient. The paper investigated efficient classifiers on data mining to integrate with the proposed expert mobile agent. The proposed expert mobile agent (EMA) uses neurofuzzy classification for consultation of patient. On first phase, the fuzzy system of EMA is trained from the previous data of other patients. A pre-processing method based on the profile of an expert is used to filter the relevant data from its expertise. After the training, the EMA are deployed to execute classification of data. Result from cross-validation test shows that the neuro-fuzzy classification provides a high accuracy in classifying the data compared to other highly accurate classifiers.

2

Related Works

Intelligent agent for healthcare plays a crucial role on giving correct information for diagnosis and providing immediate medical services. Home healthcare services provide information to a consumer of the necessary diagnosis and continuous monitoring of patient to acquire immediate response and save lives in case of abnormal indications. Agent-based intelligent decision support is proposed for the home healthcare environment [7]. The multi-agent platform is combined with artificial neural network for the intelligent decision support system in a group of medical specialists collaborating in the pervasive management of care for a patient. Mobile agents are used to serve the collaboration of services for mobile users [8]. An agent is an autonomous, social, reactive and proactive entity, sometimes also mobile. Since telemedicine is grounded on communication and sharing of resources, agents are suitable for its analysis and implementation, and these are adopted for developing a prototype telemedical agent. Data mining aims to extract interesting information from large databases is used for decision support in the field of medicine. In order to have mobility, data mining framework for mobile environment are proposed by researchers [9, 10, 11]. A contextawareness on data mining is used to maximize the adaptive capacity of data mining [9]. The use of decision support PDA supported by data mining facility can be a great

asset to the medical professionals while working on an emergency or while rushing to attend an emergency. Data mining in mobile environment using mobile agents is found in the work of Lee, et. al. [10]. This is done by sending a mobile agent to the LBS and then it performs the classification mining in the database. In the HCARD model of Gerardo, et. al. [11], proposed an Integrator agent to perform knowledge discovery in the heterogeneous server in the distributed environment. Data mining are essential in extracting rules from databases and provide decision support knowledge in healthcare environment. 2.1

Neuro-fuzzy Classification

Fuzzy systems are used to handle uncertainty from the data that cannot be handled by classical methods. It uses the fuzzy set to represent a suitable mathematical tool for modeling of imprecision and vagueness [12]. The pattern classification of fuzzy classifiers provides a means to extract fuzzy rules for information mining that leads to comprehensible method for knowledge extraction from various information sources. The fuzzy algorithm is also a popular tool for information retrieval. Fuzzy c-means classifier (FCM) uses an iterative procedure that starts with an initial random allocation of the objects to be classified to c clusters. Neuro-fuzzy systems are the hybrid of artificial neural networks and fuzzy systems. The algorithm borrows the learning ability of neural networks to determine the membership values. It is among the most popular data mining techniques used in recent research [13, 14]. There are many types of neuro-fuzzy rule generation algorithm [15]. FuNE-I is a neuro-fuzzy model that is based on the architecture of feed-forward neural network with five layers which uses only rules with one or two variables in antecedents [16]. A Sugeno-Type neuro-fuzzy system is used for a scheme to construct an n-link robot manipulator to achieve highprecision position tracking [17]. A neuro-fuzzy classification (NEFCLASS) is a fuzzy classifier that creates fuzzy rule from data by a single run through the data set [14].

Fig 1. A NEFCLASS system with two inputs, five rules and two output classes

3

Framework of Ubiquitous Healthcare based on Multi-agents

In this study, we propose a framework for the ubiquitous healthcare. The proposed framework consists of multi-agents managing the hospital shown in Figure 2. A ubiquitous healthcare in [18] proposes a method of accessing healthcare services by individual consumers applying to mobile computing device. The OnkoNet Mobile Agents Architecture was developed and consists of cooperation protocols, inference model and health ontology to provide efficient ubiquitous healthcare environment. In our framework, mobility support for expert mobile agent (EMA) and data mining support for the ubiquitous healthcare are considered. Figure 2 illustrates the proposed architecture based on multi-agent system. Each doctors and specialist have their own EMA. In Figure 2, there are three different rooms consists of monitor agent (MA) to monitor the readings of the sensors in the patient and triggers the room manager (RM) to communicate with the hospital manager (HM) for necessary diagnosis or actions to be taken by the doctors. The movement of EMA from room 1 to room 2 requires communication with RM to initialize the interaction to the agents inside the room. This procedure considers verification of accessing data for security.

Mobile Agent

Room 1 Hospital Admin

MA Hospital Manager

RM

Room 2

Mobile Agent

Patients Facilitator Agent

RM

Room 3 MA

Services

Patients

RM

MA

Patients

Fig. 2. Framework of ubiquitous healthcare based on multi-agent 3.1

Components of the Multi-agents

Hospital Manager The framework of ubiquitous healthcare in Figure 2 is consists of multi-agents to have an efficient service through the ubiquitous healthcare system. The main software agent in the framework is the hospital manager (HM). It concerns in managing the services in the hospital supporting the decision making and management. Moreover, it communicates to other agent component through facilitator agent. The deployments of the services in rooms are done by the hospital manager.

Facilitator Agent The main function of the facilitator agent (FA) is a broker between the HM and room manager (RM). All negotiations of requesting services from the room manager are done with the FA before the HM deploys its service in the room. The confirmation of deploying the services are received by FA and the RM is informed if the request of service is possible or not. Also, FA receives the alert message from the room manager if there are needs of attention with the patient. Room Manager The framework of the ubiquitous healthcare is consisted of physical room for the patients shown in Figure 2. A room manager (RM) coordinates the task of agents within the room. The software agents communicate to RM for every event that needs attention of the individuals inside the hospital. RM also request for services needed by the patients to hospital manager via FA. After the negotiation of FA, the service is deployed and adds to the RM. Monitor Agent Healthcare sensors and other sensors used for monitoring the patient are handled by monitor agents. These are programmed to detect abnormal patterns from readings of the patient. This study assumes that these sensors are used for monitoring of patient and send the signal for analysis. Service Modules In our proposed system, the services modules are used to support the diagnosis of patient, decision making and management of the hospital. These are managed by the HM. FA negotiates the request from the RM before HM deploys the service in the RM. Expert Mobile Agent The expert mobile agent (EMA) uses the proposed framework. Doctors and specialist uses their PDA as the host of EMA. The main function of the EMA is to help on the diagnosis of a patient by checking the current data and processed it with the data mining tool. EMA moves to all allowed patient for the service. Before deploying, the EMA request verification to RM for security reason so that the data will not be altered by malicious attack. Mobility middleware User virtual environment

Mobility virtual terminal

Virtual resource management

Mobile agent core services Communication

Migration

Naming

Security

Interoperation

Persistency

Java virtual machine

Heterogeneous location based services

Fig. 3. Proposed mobile agent middleware

Data mining Support

3.2

Mobile Agent Middleware

Mobile agent-based middleware is one of the issues of research for providing an advanced infrastructure that integrates protocols, mechanism, and tools to permit communication to mobile agents. SOMA in [19] discusses more issues of the mobile agent middleware. In our research, the design of mobile agent middleware is a Java-based platform. The infrastructure is divided in layers of service for designing, implementing, and deploying mobile agent-based applications. As shown in Figure 3, our proposed middleware consists of four layers. We focused more on the last component which is the data mining support. This additional service is provided to operate the data mining of the mobile agent on the data of patient.

4

Data Mining Model for Diagnosis Support

The expert mobile agent or EMA performs the consultation to the patients for advance diagnosis which is based on the proposed data mining model. Our data mining model has two phases shown in Figure 4. The first phase includes the training of EMA’s fuzzy system based on data pre-processing by selecting relevant information from the profile of an expert. Also, the training phase provides EMA to have an accurate classification based on the expert profile of the doctor or specialist. The second phase processes the data of the patient to neuro-fuzzy classification. The procedure is done by deploying the EMA from the PDA of the doctor in the room and classifies the data of patient. The security configuration of deployment is also considered in this process. After the process, the results are returned display the result of consultation.

Set the Property of the Mobile Agent based on

Train the Fuzzy Classifier of Mobile Agent

Expert Mobile Agent

History Data of Patient

Phase 1: Training

Deploy and read data of Patient

Return to the Mobile device

EMA

Classify using the trained EMA

Phase 1: Classifying

Patient’s Data

Result of Classification

Fig. 4. Data mining model using neuro-fuzzy for support of diagnosing the patient

The proposed data mining approach considers the profile of an expert in the mobile device as the basis of extracting the relevant information from Phase 1. Now let us consider the set of profiles that will be used in the preprocessing data mining: P = {p1, p2… px}. After collecting the profiles, the mobile agent uses these features to select the relevant attributes of C where it is the raw data from the patient history database. Let D as the set of the selected tuples from C. Equation 1 represent the pre-processing algorithm. The following are the phases of our proposed algorithm. n

D=

∑ C {c , c 1

2

,..., c n }

(1)

i =1

where cn attribute ( value ) = p x ( value ) Let us say the EMA is a cardiologist then the cases related to heart disease are gathered by D. D is used to train the fuzzy system of EMA. The structure of the neurofuzzy system consists of three layered perceptron. The 1st layer is for inputs (U1 = {x1,…, xn}), 2nd layer is for generating rules (U2 = {R1,…,Rk}), and 3rd layer is an output layer (U3 = {c1,…,cm}). The system also contains weights from the input layer (U1) to rule layer (U2) and from rule layer (U2) to the output layer (U3). Each connection between units xi ∈ U1 and Rk ∈ U2 is labeled with a linguistic term A Ajr(i) (jr ∈ {1,…,qi}). The values from the input layer are mapped through the fuzzy sets of the weights. W(R, c) ∈ {0, 1} holds for all, R ∈ U2, c ∈ U3. The values from the input and rule layer are evaluated in the connection of the hidden and output layer. For all output units, c ∈ U3 the net input netc is calculated Equation 2. netc =

∑

R∈U 2

W ( R, c ) ⋅ oR

∑ W ( R, c )

(2)

R∈U 2

To train the fuzzy sets from the input, Equation 3 is used. After the training, the EMA is ready to classify the data from the patient. A Java codes is shown in Figure 5.

δ R = o R (1 − o R )∑c∈U W ( R, c)δc 3

INPUT: profile, preprocessdata OUTPUT: NeurofuzzyClassification(preprocessdata) public class ExpertMobileAgent extends Aglets { public void Preprocess(String[] profile, String[] val) { while(rsData.next()) { if rsData.getObject(profile)=val; AddInfo(rowset) } NeurofuzzyClassification(preprocessdata); } public void ClassifyData(int in1, double[] pattern) { } }

Fig. 5. Neuro-fuzzy classification algorithm integrated in EMA

(3)

5

Simulation Result

The proposed framework of multi-agents was simulated using the JADE platform. Neuro-fuzzy algorithm was coded in Java and embedded it to the expert mobile agents. The environment OS platform used here are Windows OS, Red Hat Linux and Sun Solaris 8 to simulate the heterogeneity of system. To test the performance of algorithms, we used data mining tools which are the NEFCLASS and Weka data mining. We chose the data of heart disease from UCI machine learning repository used by the machine learning community for the empirical analysis of machine learning algorithms. 5.1

Classification Accuracy

Precision and recall are two typical measures for evaluating the performance of information retrieval systems. Given a discovered cluster γ and the associated reference cluster Γ, precision (PγΓ) and recall (RγΓ) applied to evaluate the performance of clustering algorithms. In classifier algorithm, recall and precision is performed by cross-validation test of the classified instances. To evaluate the performance of the algorithms, these measurements were used. This is done by calculating the average precisions in Equation 4 where AvgP is the summation of precision (Pn) of classes divided by the number of classes. Average of recall is computed in Equation 5 where AvgR is the summation of recall (Rn) of classes divided by the number of classes. The number of correctly classified instances was used to determine accuracy. The processing time of modeling of the algorithm and cross-validation of the classifier were observed to determine the time constraint and classification accuracy respectively. The classical methods used for comparison are simple logistic (SL), multi-layered perceptron (MLP) and classifier decision tree (J48) [9, 10] which are highly accurate classification methods.

5.2

AvgP

∑ =

AvgR

∑ =

n i =1

Pn

n n i =1

n

Rn

(4) (5)

Result

Comparison of classical methods for performance is shown in Figure 4. The bar graphs present the comparison of processing time and accuracy of neuro-fuzzy classification and other classical methods. In Figure 4a, the processing time of neuro-fuzzy is much faster than the MLP while SL and J48 classifier has less processing time. In accuracy, we can justify the performance of neuro-fuzzy is better than the other classical methods in the sense that even though it has a high processing time than the SL and J48, it is more accurate of classifying patterns shown in Figure 6b.

A c c u ra c y 1 0 0

90

9 0

80

8 0

70

7 0

60

6 0

Percent %

Time in Seconds

Processing Time 100

50 40

5 0 4 0

30

3 0

20

2 0

10

1 0

0

0

NF

MLP

(a)

SL

J48

N F

M L P

(b)

S L

J4 8

Fig. 6. Bar graphs showing the processing time and accuracy of each algorithm The result of precision and recall are presented in Table 1 and 2, respectively. It is important that the classifier has a high accurate in classification to be used in consultation procedure of EMA. Neuro-fuzzy has the highest precision which has an average of 0.91 and recall which has an average of 0.91 compared to MLP (0.81, 0.83), and SL (0.38, 0.35), and J48 (0.77, 0.77). Most of these classical methods were able to predict testing data with the number of misclassified patterns between 51 to 63 while neuro-fuzzy has only 25 misclassified patterns out of 270 tuples. Table 1. Precision

Classes present absent Average

NF 0.89 0.92 0.91

MLP 0.828 0.79 0.809

SL 0.843 0.821 0.832

J48 0.793 0.742 0.768

SL 0.86 0.8 0.83

J48 0.793 0.742 0.768

Table 2. Recall

Classes present absent Average

6

NF 0.90 0.91 0.91

MLP 0.833 0.783 0.808

Conclusion

Ubiquitous healthcare shows more researchable topics and it includes the integration of multi-agent systems. In this paper, we present the framework of ubiquitous healthcare based on multi-agent which supports the mobility and data mining of the mobile agent. We propose the expert mobile agent (EMA) that performs data mining to support the diagnosis of a patient. The EMA uses the neuro-fuzzy to process the consultation function. Also, a pre-processing of the relevant data based on the expert profile is shown to train the fuzzy system more efficiently. Result from simulations shows that neuro-fuzzy outperformed other high accurate classifiers. Future work will be more on the functionality of the proposed multi-agent framework in ubiquitous healthcare.

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