Article
 ChingKan Lo, HsingChung Chen, PeiYuan Lee, MingChou Ku, Lidia Ogiela, ChengHung Chuang
 Smart Dynamic Resource Allocation Model for PatientDriven Mobile Medical Information System Using C4.5 Algorithm
 Journal of Electronic Science and Technology, 2019, 17(3): 231241
 http://dx.doi.org/10.11989/JEST.1674862X.71018117

Article History
 Manuscript received January. 13, 2017
 revised May. 31, 2017
H.C. Chen and C.H. Chuang are with the Department of Computer Science and Information Engineering and the Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354 (email: cdma2000@asia.edu.tw; chchuang@asia.edu.tw);
P.Y. Lee and M.C. Ku are with the Department of Orthopedics, ShowChwan Health Care System, Changhua 50008 (email: b1208@ms26.hinet.net; showjoeku@gmail.com);
L. Ogiela is with the Department of Applied Informatics, Akademia GórniczoHutnicza University of Science and Technology, Krakow 30059 (email: logiela@agh.edu.pl)
With increasing number of autonomous heterogeneous devices in the mobile networks, an efficient resource allocation scheme is required to maximize network throughput^{[1]}, memory, and energy optimization^{[2]}, so as to achieve higher efficient and better performance. Mobile health (mHealth), which is the usages of mobile computing together with communications technologies in health care and public health, is a rapidly expanding ehealth^{[3]} area, such as the usages of applications (apps) for postoperative followup in orthopedic surgery patients^{[4],[5]}. There are huge potential demands for mHealth interventions to obtain the beneficial effects of health, the delivery processes of health service as well as the improvement of the working time and satisfaction of the nurses^{[6]}. In a wireless sensor network (WSN), the usages of resources are usually highly related to the execution of tasks which consume a certain amount of computing and communications bandwidth^{[7]}. Due to the limitations of resource availability and communications medium, these existing algorithms cannot be directly addressed for the requirements of the mHealth system.
In addition, decision tree methodology has become more popular in medical researches. Some examples may include a predictive computerassisted decisionmaking system for traumatic injury using machine learning algorithms^{[8]} and diagnosis of coronary artery stenosis. The decision tree method is a powerful statistical tool for classification, prediction, interpretation, and data manipulation, which has several potential applications in medical research^{[9],[10]}. The Waikato Environment for Knowledge Analysis (Weka) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand^{[11]}. It is a workbench that contains a collection of visualization tools and algorithms for data analyses and predictive modeling together with graphical user interfaces for easily accessing to these functions^{[11]}.
C4.5 is an algorithm used to generate a decision tree developed by Quinlan^{[12]}. C4.5 is an extension of Quinlan’s earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Weka’s techniques are predicated on the assumption that the data are available as a single flat file or a relation, where each data point is described by a fixed number of attributes (normally, numeric or nominal attributes, but some other attribute types are also supported)^{[11]}.
The rest of this paper is organized as follows: In Section 2, the related works are reviewed. Section 3 introduces the proposed algorithm model in detail. In Section 4, the simulation results of the proposed are presented and the algorithm’s performance is discussed. Finally, in Section 5, conclusions are made.
The goal of this paper is to create a smart and dynamic resourcemapping management table for the medical management information systems (MMISs) to optimize the weights of each server.
2. Related WorksIn general, network resources, storages, and energy allocation are a fundamental challenge in the mHealth system due to their unique features. Most of those traditional solutions do not consider resource consumption during communications and task execution. Therefore, they cannot be implemented efficiently. Furthermore, the allocation becomes a topic that remains largely unexplored for the mHealth system.
Several algorithms^{[13][15]} have been proposed for the task allocation and scheduling problem. Giannecchini et al.^{[13]} proposed an online task scheduling mechanism called CoRAl to allocate the network resources, between the tasks of periodic applications in WSNs^{[16],[17]}. Xie and Qin^{[14]} proposed another allocation strategy called balanced energyaware task allocation (BEATA) for collaborative applications running on heterogeneous networked embedded systems. Their strategies aimed at making the best tradeoffs between energy savings and schedule lengths^{[14],[16],[17]}. Lee and Jeong^{[15]} proposed a fuzzy relevancebased cluster head selection algorithm (FRCA) to solve problems, such as energy consumption, transmission rate reduction, decrease in throughput, and incorrect cluster head election.
However, there is little literature discussed a decision tree algorithm model to allocate multiple, heterogeneous resources in the mHealth system. This paper proposes to construct a decision tree more efficiently by reducing the incorrectness and ambiguity in the selection of service’s resources.
3. Resource Allocation Using Modified C4.5 AlgorithmIn this section, there are three subsections consisting of the machine learning analysis (MLA) using the C4.5 algorithm, the definition of symbols and formula, and the smart allocation algorithm using the modified C4.5 algorithm.
3.1 MLA Using Modified C4.5 AlgorithmThe C4.5 algorithm is applied to MLA in our algorithm as shown in subsection 3.1, which is one of the best decision tree algorithms^{[12]}. It builds decision trees using the concept of information entropy from a set of training data in the same way as iterative Dichotomiser 3 (ID3). The training data are a set of already classified samples. At each node of the tree, C4.5^{[12]} chooses the attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. The splitting criterion is the normalized information gain (difference in entropy). The attribute with the highest normalized information gain (νγ) is chosen to make the decision. The algorithm then recurs on a smaller sub list.
Weka freeware with the J48 classifier^{[12]} selected for the C4.5 algorithm is used in this study. It does not require the discretization of numeric attributes, in contrast to the ID3 algorithm from which C4.5^{[12]} has evolved Microsoft Excel 2010 for statistical computation of the formula in comparing with the Weka. The entropy, split entropy, normalized information gain (NIG), and visualization of tree structure will be evaluated and shown.
3.2 Definition of Symbols and FormulasSome symbols and formulas applied in this paper are defined below.
1) g is the generation of algorithm, where g
2) n is the collection of resources or attributes to allocate.
3) m is the collection of the node, where m
4) i is the attribute index, and its value is from 1 to n.
5) j is the attribute value index from 1 to its final value that depends on its attribute (refer to Table 1).
Attribute  Attribute value 
1. Picture archiving and communications system (PACS)  1. Magnetic resonance image (MR) 
2. Computed tomography (CT)  
3. Xray (XR)  
4. Echography (Ec)  
2. Laboratory information system (LIS)  1. Culture (Cu) 
2. Biochemistry/serology (Bc)  
3. Hemogram (Hm)  
3. Nursing information system (NIS)  1. Vital sign chart (VS) 
2. Nursing education (NE)  
3. Nursing record (NR)  
4. Pharmaceutical management information system (PMIS)  1. Inpatient stat order (IS) 
2. Inpatient regular order (IR)  
3. Outpatient stat order (OS)  
5. Report information system (RIS)  1. Consultation sheet (CS) 
2. Inpatient note (IN)  
3. Image report (IR)  
6)
7) X^{g}^{,}^{m} is the total instance in the gth generation at the node m, where m
8) A is a number represented as the total counts of optimal performance for X^{g}^{,}^{m}, similarly, A_{i} is that for X_{i}, and A_{i,j} is that for X_{i,j}.
9) B is a number represented as the total counts of suboptimal performance for X^{g}^{,}^{m}, similarly, B_{i} is that for X_{i}, and B_{i,j} is that for X_{i,j}.
10) α^{g}
11) β^{g}
12) Set ệ^{g} and ệ
13) Set ệ
14) Set ệ
15) Set
16) Set
17) Set
18) κ is the node or relevant attribute with
19) Set
20) Set
21)
22)
23)
The purpose of this study is to tune the weight for resource allocation in accordance to β^{g}, θ_{κ}, and the cumulative of B_{i} from the bottommost node resource to its parent node and substantially to the uppermost root node. The updated weight is the sum of
Using the new weight for resource allocation, another iterative of generation g+1 will restart the above algorithm to tune the weight, so as to minimize β^{g}. And β^{g}<0.05 is set as a significant optimization or stop criteria of the algorithm in this paper.
4. Experimental Results and AnalysesIn MMIS, the five major resources of HIS will be the attributes of the modified C4.5 algorithm in a cooperation communications system, including PACS, LIS, NIS, PMIS, and RIS, where i equals to 1 to 5 as its ID, individually. The class of concepts is scored as A if the performance of MMIS is optimal or B if the performance is suboptimal. The details of the attributes and attribute values are shown in Table 1.
Moreover, the modified C4.5 algorithm is processed by three phases consisting of the root generation phase, child nodes and decision tree generation phase, and weight updating for resource allocation phase. The details are described below.
4.1 Root Generation PhaseThe follows are the processed steps by using the modified C4.5 algorithm. The details are listed below.
1. γ
2.
3. X^{0,0}
4. A
5. Entropy of parent node:
ệ^{0}
6. Entropy of MR:
ệ