Reexamination of risk criteria in dengue patients using the self-organizing map

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1 Med Biol Eng Comput () 8:9 DOI.7/s7-9-6-x ORIGINAL ARTICLE Reexamination of risk criteria in dengue patients using the self-organizing map Tarig Faisal Mohd Nasir Taib Fatimah Ibrahim Received: May 9 / Accepted: 7 November 9 / Published online: 7 December 9 Ó International Federation for Medical and Biological Engineering 9 Abstract Even though the World Health Organization criteria s for classifying the dengue infection have been used for long time, recent studies declare that several difficulties have been faced by the clinicians to apply these criteria. Accordingly, many studies have proposed modified criteria to identify the risk in dengue patients based on statistical analysis techniques. None of these studies utilized the powerfulness of the self-organized map (SOM) in visualizing, understanding, and exploring the complexity in multivariable data. Therefore, this study utilized the clustering of the SOM technique to identify the risk criteria in 9 dengue patients. The new risk criteria were defined as: platelet count less than or equal, cells per mm, hematocrit concentration great than or equal % and aspartate aminotransferase (AST) rose by fivefold the normal upper limit for AST/alanine aminotransfansferase (ALT) rose by fivefold the normal upper limit for ALT. The clusters analysis indicated that any dengue patient fulfills any two of the risk criteria is consider as high risk dengue patient. Keywords Self-organizing map Clustering Dengue fever Risk criteria World Health Organization T. Faisal F. Ibrahim (&) Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 6 Kuala Lumpur, Malaysia fatimah@um.edu.my T. Faisal tarig_8@yahoo.com M. N. Taib Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia dr.nasir@ieee.org Introduction Dengue fever is widespread in many parts of the world, especially in the tropical and subtropical regions [, 6, 8,, ]. WHO classifies the dengue infection as dengue fever (DF) and dengue hemorrhagic fever (DHF)/dengue shock syndrome (DSS). Typically DF begins with a sudden temperature increase accompanied by headache, myalgia, macular rash, loss of appetite nausea, vomiting, abdominal pain, metallic taste of food, change in psychological state, and moderate thrombocytopenia [7]. Some DF patients might progress to DHF due to the increase in the vascular permeability. Typically, the DHF patients presence with some of the hemorrhagic evidence. According to the WHO, the first sign of DHF is fever or history of acute fever lasting between 7 days []. The second sign is the hemorrhagic tendencies evidenced by at least one of the following: positive tourniquet test (TT), petechiae, purpura, ecchymoses; bleeding from mucosa, gastrointestinal tract, injection sites or other location; haematemesis or melena. Third sign is thrombocytopenia (, cells per mm or less). Lastly, hemoconcentration (% or more rise in the hematocrit (HCT) value relative to baseline average for the same age, sex, and population) or sings of plasma leakage such as pleural effusion, ascites, and hypoproteinaemia. Moreover, the WHO classified DHF patients into four categories. The DF patient who has fever and hemorrhagic manifestation (indicated by only positive tourniquet) is considered as DHF I. DHF II is the DF patient who has spontaneous bleeding plus the manifestations of DHF I. DHF III is the DF patient who has the signs of circulatory failure (rapid/ weak pulse, narrow pulse pressure, hypotension, cold/ clammy skin). Finally, DHF IV is considered as a DFIII patient who profound shock with undetectable blood

2 9 Med Biol Eng Comput () 8:9 pressure or pulse. Both DHF III and DHF IV are considered as DSS; this is a fatality stage. Even though WHO criteria have been used for long time, recent studies have shown that several difficulties have been faced by the clinicians to apply these criteria [, ]. Shibani et al. reviewed the classification of dengue disease in the literature published between 97 and. The study found that the majority of the clinicians reported difficulties in applying all of the four criteria of the DHF cases []. The study suggested re-visiting the WHO criteria. Another study revealed that there is overlapping in the major clinical features that differentiate between the children with DF and DHF []. The study suggested that urgent research need to be conducted to understand the pathophysiologic mechanisms underlying the various clinical manifestations seen in dengue infections. To overcome those difficulties, many studies [,,, 9,,, ] were conducted to determine the risk criteria or risk factor in dengue patients. All these studies used statistical analysis techniques to determine the risk criteria in the dengue patient. This study aimed to employ a new approach for reexamine the risk criteria in dengue patient based on unsupervised learning technique. One of the most powerful aids for visualizing, analyzing, and understanding the complexity of the high-dimensional data is self-organizing map (SOM) technique. It maps high-dimensional data into a simple low-dimensional display so that it can simplify the complexity of the data. Accordingly, clustering the SOM technique was employed in this study. Clustering the SOM technique involves two stages; at the first stage, the SOM is employed to cluster the dengue patients data in order to visualize the common features of the data. In the second stage, the K-mean clustering algorithm is implemented to cluster the map s prototypes. To validate this technique, the obtained results were compared with the results obtained by implementing the K-mean clustering technique directly to the data. The advantages of clustering the SOM technique comparing with other conventional clustering techniques (K-mean clustering technique) are that extra compression and better separation for the clusters, visualizing the variables that makes the cluster different from others, illustrating the dimension of the data in each cluster, and demonstrating the relations among the variables in the clusters. The study was conducted by utilizing clinical data for a total of 9 hospitalized dengue patients. Methods. Risk criteria for dengue patients In order to overcome the limitation of the WHO criteria, many studies were conducted to determine the risk criteria or risk factor in dengue patients. Taweewong et al. claimed that the risk factors of DSS are bleeding, secondary dengue infection, and hemoconcentration of more than %. The study recommended that any DHF patient who has one of these criteria should be closely observed for early signs of shock []. M. Narayanan et al. declared that one of the criteria to hospitalize the suspected dengue cases is platelet count less than, cells per mm, since those patients will have high tendency to develop complications. The study suggested that more research need to be done to confirm this finding []. Conversely, Lucy et al. did not support the use of the of a platelet count value of less than cells per mm as an admission criteria in dengue infection since severe hemorrhage in DHF/DSS was not only caused by thrombocytopenia. Instead, the study recommended that the strongest risk factors for hemorrhage in DHF/DSS are extended duration of shock and a hematocrit within the normal-low range at the time of shock [9]. So, Shivbalan et al. used the combination of fever, hemoconcentration, platelet count less than, cells per mm and elevated ALT to predict the spontaneous bleeding which reflect hemorrhagic tendency in dengue patients []. Kalayanarooj et al. suggested that a positive TT, leukopenia, neutropenia, monocytopenia, and elevations of plasma AST levels can use to establish the clinical algorism for the high risk classification []. Based on statistical analysis and literature reviews, Ibrahim et al. classified the severity of the dengue disease to two groups: high risk patients and low risk patients. The classification was done based on the following criteria: Platelet (PLT) count less than or equal to, cells per mm, hematocrit (HCT) increase by more than or equal to %, and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels rose by fivefold the normal upper limit for AST and ALT. The study declare that any patient has more than one criteria is consider in the high risk group other than this is considered as low risk group [, ].. Self-organizing map The self-organizing map is an unsupervised learning technique. It consists of two layers of neurons. This type of neural network does not require any targets or outputs for learning. It receives a number of different multivariable input samples, discovers the significant relation among those samples, and presents them into two-dimensional map or display. The map consists of different clusters. Each cluster combines all the samples having the similar variable. Similarly like any other neural network, the first step in constructing the self-organizing map is initializing the synaptic weights of the network. Once the network has been initialized, there are three steps involved in the

3 Med Biol Eng Comput () 8:9 9 construction of the self-organizing map Competition, cooperation, and synaptic adaptation stages [].If the input sample vector and the weight vector can be represented by x o ¼ðx o ; xo ;...; xo m Þ o ¼ ;...; q and w j ¼ðw j ; w j ;...; w jm Þ j ¼ ;...; N ðþ where x is the input sample vector, o is the input pattern number, q is the numbers of the inputs pattern, w j is the synaptic weights, j is the neuron number, N is the total number of neuron, and m is the dimension of the input sample vector) Then, the three stages for constructing the SOM is performed as follows:. Initialize the weight vectors w j () with at the time step t = random value in the region of [,].. Select an input pattern x o from the input space randomly.. Find the best matching or winning neuron k at time step n (n =,,,, ) by using the Euclidean minimum-distance criterion kðxþ ¼arg minx o ðnþ w j ðnþ : ðþ j. Update the synaptic weight vectors of all the neurons using w j ðn þ Þ ¼w j ðnþþgðnþðh j;kðxþ ÞðnÞðx o ðnþ w j ðnþþ: ðþ h jkðxþ ðnþ ¼exp gðnþ ¼g o expð n s Þ d j;k d ðnþ! dðnþ ¼d o exp n s : ðþ ðþ where g(n) is the learning rate, d j,k is the lateral distance between the winning neuron k and the excited neuron j, d is the width of the topological neighborhood, s s is the time constant, h jk(x) (n) neighborhood function both g(n)and h jk(x) (n)are varied dynamically through time [6].. Select another input pattern and return to step (). The learning process will not end until all the inputs pattern have been provided to networks (o = q). 6. Let n = n? and repeat step () to step () until there are no noticeable changes in the feature map are observed. After the network is trained, the final SOM is visualized using unified distance matrix method (U-matrix). According to the above algorithm, there are three initial parameters chosen for constructing the SOM: topology of the map, learning rate g(n), and neighborhood h jk(x) (n). In this study, the learning rate g(n) was initialized as. in the ordering phase and. in the convergence phase. The neighborhood h jk(x) (n) was chosen as Gaussian. Usually, in the ordering phase the starting neighborhood radius can be set as. of the SOM size and decreased with the time to neuron in the convergence phase [6]. Therefore, in this study, the radius of neighborhood function in the ordering phase was varied with respect to the defining map size, and it decreased with the time until it reached to neuron in the convergence phase. The neurons connections among each other define the structure or the topology of the map. Typically, two types of the neurons network topology can be used: rectangular lattice structure or hexagonal lattice structure. Hexagonal lattice was used in this study since it gives better visualization [7]. Usually, the size of the SOM defines the topology of the data. If the map is too large, the data will be separated on the map. On the other hand, very small map will not give clear view on the data. No any direct method can define the size of the map. However, two parameters can be used to define the quality of the map: quantization error and topographical error [6]. Quantization error reflects the map resolution. It can be calculated by measuring the mean of the Euclidean distance between each data vector and their best matching unit (BMU) as follows: qe ¼ X xj k xj ð6þ m where k xj is the best matching prototype of the corresponding data vector x j. Topographic error reflects the proportion of all data vectors for which first and second best matching units (BMU) are not adjacent vectors. It can be measured by the following formula te ¼ N X N i¼ uðx~ i Þ: ð7þ The function uðx~ i Þ is, if ðx~ i Þ data vector s first and second BMUs are adjacent and otherwise.. Clustering Clustering is an unsupervised technique [, 9] used for grouping a set of data or samples into a number of subsets known as clusters. Each cluster contains a group of samples sharing the similar features or similar measurements. Many algorithms have been developed for clustering the data, but most of those algorithms are based on two main algorithms: hierarchical and partitive algorithms []. One of the most common used partitive algorithms is the K-mean algorithm proposed by Macqueen []. The K-mean algorithm aims to minimize the error function E ¼ Xc X kx c k k ð8þ k¼ xq k

4 96 Med Biol Eng Comput () 8:9 where c is the number of the clusters, c k is the center of cluster k, x is the data sample belongs to cluster Q k The optimal number of the clusters can be determined through some kind of validity index. In this study, the Davies Bouldin validity index [] was used since it can be utilized through in the SOM toolbox. The Davies Bouldin index for c clusters can be determined from the following formula DBðcÞ ¼ c X c k¼ max k6¼l S c ðq k ÞþS c ðq l Þ dðq k ; Q l Þ ð9þ where S c (Q k ) is the intra-cluster distance of cluster, Q k, S c (Q l ) is the intra-cluster distance of cluster Q l,andd(q k, Q l ) is the inter-cluster distance between two clusters Q k and Q l. The Davies Bouldin validity index is capable to tell us the best number of clusters for the current data set such as the clustering with the smallest index indicates good clustering results.. Two-level approach for clustering the data Clustering the SOM technique was employed in this study [7]. This technique aimed to cluster the SOM rather than the data directly. Generally, the implementation of this technique is performed in two stages: First, the SOM is trained to identify the prototypes of the data set, and second, the K-mean clustering technique is implemented to cluster those prototypes. The advantages of this method comparing with other clustering methods are [7]. Reducing the computational cost even when small number of samples is used.. Reducing the noise since the clustering is performed using the prototypes which are the local averages of the data. The other advantages of visualizing the SOM at the first stage instead of directly implementing the K-mean clustering are extra compression and better separation for the clusters, showing the variables that make the cluster different from others, illustrating the dimension of the data in the each cluster, and demonstrating the relations among the variables in the clusters. The combination of SOM and K-mean clustering technique has been implemented in various applications [,, 8, 9].. Implemented system In order to reexamine the risk criteria in dengue patients and classify them, the SOM was used to identify the prototypes of the samples, and K-mean clustering method was utilized to cluster those prototypes. The study was conducted by employing the SOM toolbox [8]. The implemented technique is shown in Fig.. Samples collection and normalization SOM training Selection of the best map size K-mean clustering Selection of the best clusters Clusters analysis Define risk criteria New classification of dengue patients Fig. The block diagram for the implemented procedures The first step is the collection and the normalization of the dengue patient s data. Data comprises of 9 dengue patients obtained by Ref. [] was used in this study. The collection of the samples was conducted from day (the day of defervescence) until day ( days after the defervescence). Defervescence of fever is defined as the day when a patient has no fever []. Normalization of the data was performed to organize the variance of the inputs. If some inputs have variance that is significantly higher than the variance of other inputs, those inputs will control the map organization. Therefore, the input data must be normalized before SOM training. The most common method for normalization the data is to normalize the variance of each variable equal to one. The following phase is the SOM training. The training meant to organize the samples having the similar variables and generate prototypes for those samples. Evaluating the quality of the maps was conducted to define the optimal map. After the best map was chosen, the K-mean clustering technique was implemented to cluster the map prototypes. Clustering the SOM is valid if the prototypes reflect the properties of the data [7]. To ensure this fact, the K-mean clustering technique was implemented directly to cluster the data, and the obtained clusters were compared with those who obtained from clustering the prototypes. Due to the fact that the K-mean is sensitive to initialization, it was run multiple times for each K and the best of these is selected based on sum of squared errors. The Davies Bouldin index was calculated for each clustering and the optimal clustering was selected, which corresponds to the lowest value. After clustering, the prototypes of the

5 Med Biol Eng Comput () 8:9 97 samples of each cluster were extracted and analyzed in order to define the risk criteria of each cluster. Finally, the dengue patients were classified as high risk and low risk dengue patients Topographic error Quantization error Clinical data/data pre-processing..6 Most of the clinical studies which have been conducted to identify the risk on dengue infection were based on finding the significant indicator of the hemorrhagic evidence. The significant parameters used in those studies were platelet count (PLT), percentage of the hematocrit increase (HCT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and white blood cell (WBC) [,, 9,,, ]. Therefore, in this study, all the above mentioned indicators were investigated except the WBC. The reason behind excluding the WBC is that in this study, the investigation of the clinical data was conducted from the day of defervescence until day ( days after defervescence). It is declared that the amount of WBC reach the normal level at the day of defervescence []. Accordingly, the WBC does not reflect any significant for defining the risk criteria. Therefore, each sample contained only three variables AST/ALT, PLT, and HCT. The identification of the risk criteria was performed by observing the variation of the above parameters in dengue patients during the days. The maximum values of the AST and ALT folders (AST/ALT) were used to determine the maximum increment in the AST and ALT folders. The minimum values of the PLT (MINPLT) were used to evaluate the lower value of the PLT. Finally, the percentages of the HCT increment (HCT) were used. The reference day for calculating the HCT percentages is the day that the patient was admitted to the hospital (base line) Map units Fig. The quantization error and topographical error with varying the map size..... Results. Validation measures of SOM size and K-mean cluster Several trainings were conducted to define the optimal map size. Accordingly, Fig. shows the values of quantization and topographical error when the map size was varied. Typically, small quantization and topographical error are preferable for defining the optimal map size. Therefore, the 7 units map was selected, since it has small quantization and topographical error,. and., respectively. The u-matrix and the component planes are shown in Fig.. The u-matrix represents the structure of the prototypes of the samples in D, while the component planes represent the corresponding value for each variable (HCT, MINPLT, and AST/ALT) in the u-matrix. At the right side Fig. Visualization of the u-matrix and the component planes of each map, there is one color scale column which indicates the numerical value in the map. As it shown in Fig., the u-matrix alone did not clearly differentiate the clusters of the prototypes. To define the structure of the clusters, the K-mean clustering technique was implemented to cluster the map prototypes. The K-mean clustering technique was also implemented directly to the data to ensure that the clusters obtained for the prototypes are reflecting the properties of the data. The algorithm was run five times for each k to avoid the problem of initialization sensitive. The optimal number of clusters was determined using the Davies Bouldin index. Figure shows the Davies Bouldin index after being

6 98 Med Biol Eng Comput () 8:9 Davies-Bouldin index. Clustering the SOM. K-mean clustering Number of clusters Fig. The Davies Bouldin index B C D Fig. The optimal clustering structure of the SOM applied to the map s prototypes and the data. Both of the indexes achieve a minimum (optimal) value for five clusters. The corresponding values of the Davies Bouldin index when the map prototypes and the direct data were used are. and.8, respectively. Therefore, the best clustering corresponds to a number of five clusters. Finally, Fig. shows the structure of the SOM after the best clustering was projected.. Clusters analysis To understand the special criteria for these clusters, the prototypes of the samples for each cluster were analyzed. Accordingly, Fig. 6 depicts the error bar charts at 9% confidence interval values for the variables in each cluster, while Fig. 7 shows the histogram plots for each cluster. Cluster C in Fig. 7 shows that the highest value of the HCT is 8%, while the lowest value is %. In this cluster, the sign of the haemoconcentration is present in all samples, since the HCT values are more than %. The value E A of the MINPLT is varying from 8 to cells per mm. The sign of thrombocytopenia is also present on those samples, since the highest value of the MINPLT is cells per mm. The ALT/AST values are not consistent, since they are varying from.-fold to 7.9-fold. In cluster D, the highest value of the HCT is 9%, while the lowest value is 6%. It is obvious that all the samples in this cluster are having high values of HCT concentration which reflect the haemoconcentration sign in those samples. The MINPLT is varying from to cells per mm. The highest value of ALT/AST is.8-fold, while the lowest value is.-fold. Cluster E shows that all the samples are having relatively high values of AST/ALT. The highest value of the AST/ALT is 6-fold, while the lowest value is 8.67-fold. The highest value of the MINPLT is 9 cells per mm, while the lowest value is 8 cells per mm which indicates the presence of thrombocytopenia sign in those samples. The value of the HCT is varying from. to %. Cluster A shows inconsistent values of the HCT, MIN- PLT, and AST/ALT. The values of the HCT, MINPLT, and AST/ALT varying from to 7%, to 6 cells per mm and 8.-fold to -fold, respectively. Finally, cluster B shows that there is relatively high value of MINPLT. The highest value of the MINPLT is cells per mm, while the lowest value is cells per mm. The values of the AST/ALT and the HCT are varying from.-fold to 6-fold and from to 6%, respectively. Discussion. Performances of the two-level approach for clustering the data As shown in Fig., the values of Davies Bouldin index when the data was used are higher than the values when the map prototypes were used. This result indicates that by using the SOM prototypes, the clusters are more compact and well separated than using the data directly. This finding is in line with the fact indicates that the K-mean algorithm performs better by using SOM prototypes than using the data directly [7]. The reason behind this is as follows: by using the SOM prototypes, the noise in the data is reduced since the map prototypes are the local averages of the data.. New risk criteria for classifying the risk in dengue patients From the clusters analysis results, it can be observed that all patients in clusters C, D, and E clusters experienced a combination of two criteria: low MINPLT values (less than

7 Med Biol Eng Comput () 8:9 99 HCT values PLT values ALT/AST values 8 8 9% CI HCT 6 9% CI MINPLT 6 9% CI AST/ALT A B C D E CLUSTERS Fig. 6 The HCT, PLT, and ALT/AST values in all clusters A B C D E CLUSTERS A B C D E CLUSTERS or equal cells per mm ); high ALT/AST values (equal or great than fivefold); high HCT values (equal or great than %). In contrast, none of the patients in clusters B and A experienced a combination of those criteria. For instance, none of the patients in clusters B experienced MINPLT value less than cells per mm or high HCT (equal or great than %). Even though some patient in cluster A experienced MINPLT value (less than cells per mm ), none of those patients experienced AST/ALT value greater than or equal to fivefold or HCT value greater than or equal to %. Therefore, the risk criteria that can differentiate the clusters are. Platelet counts (PLT) less or equal than, cells per mm.. Hematocrit concentration (HCT) great than or equal %.. Aspartate aminotransferase (AST) rose by fivefold the normal upper limit for AST or alanine aminotransferase (ALT) rose by fivefold, the normal upper limit for ALT. Based on the above criteria, the clusters can be divided into two categories: low risk clusters and high risk clusters. High risk clusters are those who contain any dengue patient experienced at least two risk criteria whereas low risk clusters are those who contain any dengue patient experienced less than two risk criteria. Therefore, clusters C, D, and E were classified as high risk clusters, while clusters A and B were classified as low risk clusters. Consequently, all patients in cluster A and B were considered as low risk dengue patients. High risk patients are those who are in clusters C, D, and E. This categorization can be generalized for all dengue patients such that any dengue patient fulfill any two of the risk criteria, is considered as high risk dengue patient. On the other hand, low risk dengue patients are those who do not fulfill any one of the risk criteria or fulfill only one of them.. Validation of the proposed criteria To validate the new risk criteria, it was compared with some other dengue researchers findings and the WHO criteria. First, the proposed risk criteria agrees with the finding of Ibrahim [] where the criteria were based on statistical analysis and exhaustive literature review from other dengue researchers findings. In both the studies, the high risk dengue patients were classified based on finding a combination of two risk criteria. However, the obtained threshold values of the PLT and HCT are slightly different in both studies. Second, the WHO criteria were compared with the proposed criteria. All dengue patients (total of 9) were classified according to the WHO criteria mentioned in Sect.. The patients were classified as follows:. patients were classified as DF patient. 87 patients were classified as DHF I patient. patients were classified as DHF II patient. patient was classified as DSS patient The same patients were classified by using the new proposed criteria. The new criteria classified include 76 patients as high risk dengue patients and 9 patients as low risk dengue patients. Table shows the results of the classification after WHO criteria and the proposed criteria been applied. The results show that the new risk criteria classified % of the patients who were classified as DF patients by using the WHO criteria as high risk dengue patients, while the remaining patients were classified as low risk patients. These results agree with some other dengue studies reported, which showed that several cases of DSS patients did not fulfill all the four criteria for DHF and they were considered as DF by applying the WHO criteria []. Moreover, Table shows that the new criteria agree with

8 Med Biol Eng Comput () 8:9 Cluster E 6 Cluster C Cluster D 8 6 Cluster B Cluster A HCT % MINPLT (X ) AST/ALT Fig. 7 The histogram plots for the clusters Table Comparison between WHO criteria and the proposed criteria Proposed criteria WHO criteria Total number of dengue patients (9) DF 87 DHF I DHF II DHF IV/DSS Lower risk patients / (67%) 7/87 (6.%) 6/ (7.7%) / (%) 9/9 (6%) Higher risk patients / (%) /87 (.) / (.%) / (%) 76/9 (9%)

9 Med Biol Eng Comput () 8:9 the WHO criteria by classifying % of the DHF IV patients as high risk dengue patients. Finally, 6. and 7.7% from the patients who were classified by WHO as DHF I and DHF II, respectively, were classified as low risk patients by using the new criteria. This result agrees with the study conducted by [] which reported considerable overlapping in the clinical manifestations of the DF patients and DHF patients. Hence, application of clustering the SOM technique may improve the identification of the risk criteria in dengue patients, by incorporating three new criteria, and also improve the clustering efficiency in terms of compression and separation by.8% when compared to the K-mean clustering technique. Acknowledgement This work is financially supported by a Malaysian Ministry of Science Technology and Innovation (MOSTI) Science Fund Project No. --- and postgraduate research Fund (PPP) No. PS8-8B, University of Malaya. References. 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