Classification of Dengue Outbreak Using Data Mining

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1 Classification of Dengue Outbreak Using Data Mining Models Noor Diana Ahmad Tarmizi Farha Jamaluddin Zulaiha Ali Othman Azuraliza Abu Bakar ABSTRACT This paper presents the Malaysian dengue outbreak detection model using three classification methods. Dengue outbreak detection and prediction has been a major interest of researchers in surveillance and public health. In this paper, a selection of different dengue data attributes are used for classification modelling and the performances are compared with the previous related work. The experimental results show that the proposed classifiers improve the performance of other methods. The significant selection of attributes in dengue dataset contributes to the good results. Categories and Subject Descriptors H.2.8 [Database Applications]: Data Mining General Terms Algorithms, Performance, Design, Experimentation Keywords Outbreak detection, classification, attribute selection 1. INTRODUCTION A classification technique, also known as a classifier is a systematic approach in developing a classification model from a set of input data. There are many classifiers such as decision tree (DT), neural networks (NN), naive Bayesian, support vector machine (SVM) and rough set theory (RST). Each classifier uses learning algorithms to discover the most appropriate model to the relationship between an attribute set and class labels of input data. The model produced by the learning algorithm should both fit the input data well and correctly predict the class labels of records that it has never seen before. Therefore, the main objective of the learning algorithm is to develop a model with good generalization potential which is a model that precisely predicts the class label in the past unfamiliar records[1]. Dengue outbreak is one of the serious contagious diseases and becoming more worrying in tropical climate countries such as Malaysia, Indonesia and Thailand. Statistical analysis from the Ministry of Health Malaysia in 2010 showed an increase of 11% of the number of cases reported with 134 deaths from the previous year. Presently, no specific treatment and vaccines available for dengue fever patients. It is critical for the public, particularly the potential vicinity of the suspected infected individuals to seek treatment immediately to avoid any complications that can lead to death. Therefore, in order to increase the effectiveness of disease prevention and control activities of dengue, this study is carried out in order to develop a predictive model for detecting epidemics. The remainder of this paper is organized as follows. The next section briefs on the related works which use the same data. The classification methods will be discussed in section 3. Section 4 presents the results and discussion from the modelling process. Finally, section 5 concludes the work in this paper. 2. RELATED WORK There are several researchers who work on dengue outbreak detection models [2-10]. Among these researcher [2-4], a study in [2] Research Notes in Information Science (RNIS) Volume12, April 2013 doi: /rnis.vol

2 was conducted on Multiple Attribute Frequent Mining Based for Dengue Outbreak. In this study, they identified several numbers of attributes to be used in determining outbreaks rather than using only case counts. The attributes used are year and epic week (week1 to week 52), age, sex, races, address, nature of work, type of dengue, incubation period, epidemic type, recurrent cases and dead code. They compared theirs with CUSUM method. The experiment is conducted using multiple attribute values based on Apriori concept. This method has shown a good performance in terms of detection rate, false positive rate and overall performance. This study has been proven through an experiment that more than one attribute can be used to better detect outbreaks. The study also found that using the maximum length of the item can show a better performance in outbreak detection based on a graph in vector-borne diseases. The overall results of the study found that the resulting algorithm can overcome the Cumulative Sum (CUSUM) in detecting outbreaks. A predictive model for epidemic detection using Multiple Rule Based Classifiers was proposed in [3]. The classifiers used are the Decision Tree, Rough Set Classifier, Naïve Bayes, and Associative Classifier. Several classifiers are investigated to study the performance of various rule based classifiers individually and the combination of the classifiers. The multiple classifiers can produce better accuracy which is up to 70% with the rules of a higher quality than using a single classifier. Mousavi [4] explores the Negative Selection Algorithm (NSA) which is based on the Artificial Immune Systems for detection of dengue outbreaks. The work reveals some weaknesses in NSA that are not able detect the normal and abnormal class well due to the nature of the data. Aside from using the dataset from previous researches, there are also several studies that contribute to the effort of avoiding the dengue disease. D. Thitiprayoonwongse et al. [5] carried out a study on dengue dataset in Thailand. They classify the data of dengue infection by using decision tree in order to detect the day of defervescence of fever or known as day0. The day0 is the critical date which often brings fatal to dengue patients. For the first experiment they are able to achieve up to 95% of accuracy. However, they obtained very low accuracy in determining the day0. In designing effective strategies of virological surveillance and public health management, V. Rao and M. Kumar [6] developed a new computational intelligence-based methodology that predicts the diagnosis of dengue in real time, minimizing the number of false positives and false negatives. One of their methodologies is by using decision tree method that employs boosting for generating highly accurate decision rules. The predictive models developed using their methodology are found to be more accurate than the state-of-the- art methodologies used in the diagnosis of the dengue fever. L. Tanner et al. [7] also employed decision tree algorithm in predicting the outcome of the dengue fever in the early phase. They use a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data. The accuracy of the model produced is 84% which can differentiate dengue from non-dengue febrile illness. This study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance. N. Rachata et al. [8] proposed an automatic prediction system of Dengue Haemorrhagic Fever(DHF) outbreak risk by using entropy technique and artificial neural network. During knowledge extraction, external factors such as temperature, rainfall and humidity are taking into consideration. Then, the data is processed by using neural network to predict the possible risk of DHF outbreak. They used weather data and DHF cases to evaluate the performance of the proposed system. The prediction achieves 85.92% of accuracy compared to the actual data. A study by G. Guthrie et al. [9] is to detect diversion by identifying changes in data sequences that are not normal. Artificial Neural Network (ANN) was used to detect changes in the flow of the counter pharmaceutical sales. Early detection of outbreaks will enable public health officials to respond more quickly to a potential pandemic. Syndromic surveillance involves the monitoring of data that can indicate illness suffered in a society based on drug purchases at one time. Comparisons are made between RNB and methods of Moving Average and found that RNB can reduce the error rate and at the same time improve the detection rate for a pandemic. F. Ibrahim et al. [10] describe a non-invasive prediction system for predicting the day of effervescence of fever in dengue patients using artificial neural network. They used multilayer feed-forward neural networks (MFNN) to develop the system which is based on the clinical symptoms. The proposed system achieves 90% prediction accuracy. F. Honghai et al [11] proposed a classification rules generation for SARS patients by using rough set. SARS is an acute infectious disease. They induced some classification rules from 30 SARS patients and 30 non- SARS patients. From the attribute reduction, micronutrients such as Fe, Ca, K and Na are necessary and sufficient for classification. They also found that micronutrient Ca has a strong correlation to the disease. Different from both neural network and decision tree techniques, dengue outbreak can also be applied to the other classification technique which is Rough Set Theory (RST) that approaches of upper and lower approximation of the data mentioned that contributes from the pair of vague and imprecise data or known as rough set [14]. This method is said to posses greater advantages in which it finds the significance and hidden data, generate minimal of rules, find minimal set of data and easy to interpret; as what the current trend are focusing on such as in medical analysis, finance, banking and other fields [15] including predicting the dengue outbreak. However, it is difficult to find a research that is using the same dengue data to do the RST classification, but there are researches related with this technique that is using different dengue data to choose the best method for knowledge acquisition for dengue dataset through the exploring of multiple reducer in RST [15]. Aside from classifying the data using RST, many researches had applied RST to do the reduction of data and generated rules from it for a better aid in the research. Based on the study of Madhu and Reddy [16], they used the RST as a reduction method for their dengue data (input parameter) to help the doctor with the dengue diagnosis based on patient s condition [17] and represent their finding through the upper and lower approximation of the dengue datasets. In this study, we use additional attributes that are different from the one used by [2-4] and aim to improve their detection performance. 3. METHODOLOGY This section presents the methodology used in the dengue outbreak detection modelling. It adopts the standard data mining methodology as the following sections. 3.1 Data Collection The original demographic dengue dataset used by [2-4] are collected from Public Health Department, Selangor State. The attributes are year, week of the year, cumulative week from year 2003 till year 2010, number of dengue fever for current week, number of dengue hemorrhagic fever for current week, total number of cases in current week, maximum temperature value, minimum temperature value, average temperature value, humidity value, rainfall, month, age, sex, race, work, address, district office in charge, district, outbreak. 72

3 3.2 Data Preparation In [2-3], eleven attributes are selected from the original data. The attributes are year, epidemic week, age, gender, address, occupatient, type of dengue, incubation period, type of outbreak, repetition case, and death code. However, in this study the data are combined with the meteorological data obtained from the Faculty of Health Sciences, University Kebangsaan Malaysia as determining factors in order to improve the quality of model produced for further process. (The additional weather atrributes are minimum and maximum temperature, minimum and maximum humidity, and rainfall. The dataset undergo the standard data preprocessing schemes such as data cleaning, attribute selection and reduction, and data transformation [1]. Table 1 shows the attributes and their description. In this study, the target class (outbreak) is based on the definition of the dengue epidemic by experts. The target class for this data set is generated by calculating the average of dengue cases (Case) between previous and current weeks (Acc_week) and compare these values with the current dengue cases. If the value of cases for the current week is higher than the previous week, then it is classified as an outbreak (1), if the cases decrease then it is classified as non-outbreak (0). Table 2 shows the cases for the particular week and example for calculation of the target class based on the information in the table. Finally, the target class of dengue dataset which is classified as Yes comprising of 3087 data and 2894 data are classified as class No. Table 1. Attributes Description No Attribute Form Description 1 Acc_week Numeric The number of dengue fever for current week 2 Df Numeric Dengue fever in that particular week 3 Case Numeric The number of cases of dengue fever and dengue hemorrhagic fever 4 Temp_av Numeric Average of temperature 5 Rainfall Numeric The rate of rainfall in that particular week 6 Humidity Numeric The rate of humidity in that particular week 7 Outbreak Category The existence of outbreak (1) or nonoutbreak(0) Table 2. Example data in week 3 week 5 Acc_week Case The calculation of the target class for Acc_week is 5: 1) Calculate the average Case of the previous week which is 3 rd and 4 th week : Average for 3 rd and 4 th week is [( ) / 2]= 111 2) Compare the average with the current week which is 5 th week, if the value of Case is higher than the average value of the case for previous week, then the target class are classified as outbreak. If the Case value is lower, then the average value of the target class are classified as No. Based on Table 5, the value of Case in 5 th weeks (Acc_week) is 67. The value is smaller compared to the average of previous week which is 111. Then the target class in the 5th week is Non-outbreak. 3.3 Model Development In this study, three classification algorithms are used namely Decision Tree (DT), Artificial Neural Network (ANN), and Rough Set Theory (RS). Readers are referred to [12-14] for detail theoretical aspect of the algorithms. The selected algorithms previously used by [3-9] but it intends to improve the accuracy based on the different selection of dengue attributes. Decision Tree (DT) is a simple classification technique and is widely used in data mining task. It is popular and powerful tools for classification and prediction. DT will classify the knowledge in the form of tree structure [12]. Neural network specifically the Multilayer Perceptron Network (MLP) used in this study is feed forward and back propagation neural network. The network is feed forward by mapping the input data set into a set of appropriate output and then propagates backward to adjust the weight using a learning algorithm. It is a modification to the linear perceptron in which it uses the value of three or more layers of neurons with nonlinear activation function. MLP can be used in various application especially classification as more flexible and non-linear models that consist of the number of units are divided into several layers [13]. The RS classifier is developed based on rough set theory for the classification purpose. RS is well known classification method that is able to handle uncertainty, noise and incomplete data. It is based on the space approximation concept and able to model the data in a form of decision table [14] to a set of classification rules. The advantage of RS is it generates a large number of rules that are easy to interpret and has benefited many real world applications. The work in [15] explores the knowledge model of RS for dengue dataset. 3.4 Experiment design The experiments are conducted as standard classification modelling setup i.e. the 10-cross validation (10-CV) and percentage split (PS) procedure. The pre processed dataset are divided into 10 different set training and testing set. The performance of the models is measured in terms of accuracy (ACC), ROC, MSE, and F-measure. For the DT and RS, the generated number of rules(nr) are recorded [18-19]. The general performance of the models is compared with other previous works that use the same data source. Although it is not significantly comparable, the overall performance of dengue outbreak detection model specifically for Malaysian dataset can potentially open new opportunity for further work. 4. EXPERIMENT AND RESULT The results in Table 3 indicate that all proposed models comparatively perform well in this study. The ROC values show that the classifiers are reliable and robust. RS model gives more benefit other than the highest accuracy. The generated rules are meaningful and interpretable by experts. Table 4 depicts the example of RS rules. The rules indicate that besides extracting common knowledge that are constructed by several known factors for dengue cases such as humidity, temperature, number of cases, and rainfall, an interesting finding is that race of the patient is also the contributing factor. It can be seen as it appears in many rules. 73

4 Table 3. Performance of the Classification Models Test Options 10-CV Model ACC (%) ROC MSE F-measure NR DT ANN RS DT PS ANN RS Outbreak Case (1) DF(19) AND Case(22) Table 4: Example of RS Rules DF(31) AND Case (35) DF(23) AND Temp_Ave(0) AND Rainfall(0.5) Case (22) AND Race(1) DF(14) AND Case (15) AND Temp_Ave (1) Case (12) AND Temp_Ave (0) AND Rainfall (0.5) AND Race (0.33) Non-Outbreak Case (0) Case (7) AND Race (1) DF(13) AND Case (15) AND Temp_Ave (0) DF(23) AND Case (24) AND Humid(1) Case (14) AND Temp_Ave (1) AND Rainfall (1) AND Race (0.67) DF(18) AND Case (18) AND Temp_Ave (1) AND Humid (1) DF(18) AND Case (18) AND Humid (1) AND Rainfall (0.5) AND Race (0.33) DF(18) AND Temp_Ave (1) AND Humid (1) AND Race (1) DF(31) AND Temp_Ave (1) AND Rainfall (1) DF(18) AND Humid (1) AND Rainfall (0.5) AND Race (1) We further evaluate the performance of the models across other previous works in accuracy in general [2-4]. These researches use similar data sources with different number of attributes and representation scheme. As mentioned in previous section, the proposed study merges several weather related attributes to the original set. Fig. 1 shows the comparative performance of various Malaysian dengue outbreak detection models. Number in parenthesis is the number of attributes used in the works. RS, ANN, DT are our proposed models while the results of other models are taken from [2-4] that uses the same data sources. The figure depicts that the proposed models outperform other four methods in terms of accuracy. It can be concluded that the selection of attributes used in this study is more appropriate than those in previous researches. On the other hand, although DT and ANN are well known methods in dengue outbreak domain, the significant selection of attributes enable the algorithms gain the highest accuracy [5-10]. Figure 1: General Performance of Malaysian Dengue Outbreak Detection Models 5. CONCLUSION This study explores the improved models for dengue outbreak detection problem. Using the new selection of attributes, the models obtained are promising with several advantages. The DT and NN models are commonly used methods in this problem while RS is a promising rules based method that provides meaningful knowledge to be further analysed by the experts. It gives a good trade off between the high classification accuracy and the generation of interesting and interpretable rules. The selection of attributes and the formulation of outbreak definition enable the classifier to outperform other models. 6. ACKNOWLEDGEMENT This work is supported by the Exploratory Research Grant Scheme (ERGS/1/2011/STG/UKM/02/49) and collaboration with Negeri Sembilan State Health Department, Ministry of Health, Malaysia. 7. REFERENCES 1. Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann. 2. Z. A. Long and A. A. Bakar, Multiple Attribute Frequent Mining-Based for Dengue Outbreak, Proceedings of the 6 th International Conference on Advanced Data Mining and Application, Part I, LNCS 6440, pp , A. A. Bakar, Z. Kefli, S. Abdullah, and M. Sahani, Predictive Models for Dengue Outbreak Using Multiple Rulebase Classifiers, Proceedings of the International Conference on Electrical Engineering and Informatics. DOI: /ICEEI July, M. Mousavi, A.A. Bakar, S. Zainudin, Z. A. Long, M. Sahani, M. Vakilian. Negative selection algorithm for dengue outbreak detection. Turkish Journal of Electrical Engineering & Computer Sciences. DOI: /elk D. Thitiprayoonwongse, P. Suriyaphol, and N. Soonthornphisaj, Data Mining of Dengue Infection Using Decision Tree, 13 th International Conference on Enterprise Information Systems (ICEIS 2011), pp , June 8-11, V. S. H. Rao and M. N. Kumar, A new intelligence-based approach for computer-aided diagnosis of Dengue fever., IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in 74

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