Knowledge-Based System for Malaria Prevention and Control : A Conceptual Model Dinda Lestarini1, Sarifah Putri Raflesia1*, Indah Puspita2, Phey Liana2, Andra Kurnianto2 1 Computer Science Faculty, Universitas Sriwijaya, Palembang, Indonesia 2 Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia Email :
[email protected]
Abstract—Malaria is one of the most important health problems in the world, particularly in malaria-endemic countries. Global efforts have been made to control and eliminate malaria, including the implementation of Early Warning Alert and Response System (EWARS). In Indonesia, EWARS is used to monitor the potential outbreaks of infectious diseases. In fact, EWARS has not generated an optimal result. In this paper, a conceptual model of knowledgebased system for malaria prevention and control is proposed. The model aims to improve malaria prevention and control process. Geo-fencing technique is also used as a means to increase public awareness and knowledge about malaria in malaria-endemic areas. Keywords— knowledge-based system, geo-fencing, malaria prevention and control
I. INTRODUCTION Infectious diseases are a threat that can affect the social and economic life of the community. Infectious disease is a disease that can be transmitted to humans and caused by biological agents, such as viruses, bacteria, fungi, and parasites [1]. Malaria is an infectious disease caused by plasmodium which is transmitted by female Anopheles mosquito [2]. According to World Malaria Report, there are 429,000 deaths from malaria in 2015. Most deaths are occurred in African region, followed by Southeast Asia and Eastern Mediterranian. The number of malaria deaths in children under 5 years is about 70% of total number of malaria globally. Despite the decreased number of deaths in children, malaria remains a major killer for children[3]. WHO developed The Global Technical Strategy (GTS), which is also accompanied by Action and Investment to Defeat Malaria (AIM), to accelerate malaria elimination. Those documents emphasize the need for interventions for malaria prevention, diagnosis and treatment with malaria surveillance as a core intervention [3]. Indonesia is one of malaria endemic countries. As malaria endemic country, malaria surveillance becomes one of infectious diseases surveillance priority in Indonesia[4] to ensure the prevention of malaria spread. Indonesian Ministry of Health has adapted Early Warning Alert and Response System (EWARS) for surveillance of infectious diseases in Indonesia. This system is used by primary health centers to report infectious diseases cases in their region. Through continuous monitoring, the system can detect the increasing trend of disease cases, especially those that have the potential to cause outbreaks in Indonesia. The system will send an alert to healthcare personnel when there is a possibility of infectious disease outbreak in an area.
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In order to predict malaria incidence trend, EWARS is highly dependent on data completeness and accuracy. Unfortunately, data completeness and accuracy rate are still below the standard set by Indonesian Ministry of Health [5][6]. It is often caused by the reporting delay from primary health centers. The reporting delay will affect the result of outbreak detection considering the outbreak threshold of malaria is determined by the number of malaria incident in the last few weeks. The implementation of machine learning is proposed to address this problem. Machine learning has a capability to learn from the past data[7] without any predefined rules[8] which can give an advantage to health authorities in determining the possibility of malaria outbreak and providing the knowledge to support the decision-making process regarding malaria prevention and control. Another problem concerning malaria is its rapid spread within countries and across national borders [9]. Each year, over 10.000 travellers suffer from malaria after visiting malaria endemic areas [10]. Travellers who visited malariaendemic countries are also possible to transmit the disease to another person. In this study, geo-fencing technique is implemented to facilitate information dissemination in malaria-endemic area. Geo-fencing technique is used to monitor people who entered and exited malaria-endemic area and provide them with warning and information about malaria. Finally, this study aims to improve malaria prevention and control process in Indonesia by means of the knowledgebased system and geo-fence technique. Machine learning is implemented in knowledge-based system to provide knowledge in decision-making process. Meanwhile, the geofencing technique supports the health authorities to increase public awareness and knowledge about malaria in malariaendemic areas. II. LITERATURE REVIEW A. Malaria Prevention and Control in Indonesia Malaria is an infectious disease which is caused by plasmodium which is transmitted by female Anopheles mosquito bite [2], but it can also be transmitted through infected blood transfusion. There are some factor that can increase malaria burden, such as population movement, overcrowded population, poor access of health services and food shortages [11]. According to Law No. 82 of 2014, infectious diseases prevention and control can be done by (1) health promotion, (2) surveillance, (3) risk factor control, (4) case finding, (5) case handling, (6) immunization, (7) the provision of mass prevention drugs, and (8) other activities established by the
minister. As an effort to reduce malaria, Indonesian Health Ministry has adapted EWARS to monitor the potential for infectious disease outbreaks in Indonesia. There are 23 types of infectious diseases reported through EWARS, including malaria. EWARS is based on case reporting in the field. Health personnels (private clinics, midwives, auxiliary health center) will report to surveillance officers at the primary health centers via Short Message Service (SMS). Surveillance officers will forward the data weekly to the district in which the primary health center is located. The data will be entered and analyzed by the districts, and will be sent via e-mail to provinces and central government using specialized software that can generate early warning according to place, time and type of illness. The alert will be generated when the number of cases of an infectious disease reached the threshold for outbreak. In a steady population, the threshold for malaria is if the number of cases reaches 1.5 times the mean of cases calculated over the last three weeks. The district will respond the alert immediately in accordance with the severity of situation. The actions that need to be done including verifying the data, investigating epidemic, checking laboratory confirmation and doing countermeasure actions.
center resulting in excessive workload hence the reporting delays and the lack of data validation. Meanwhile, the lack of involvement from some hospitals and private clinics cause the possibility of unrecorded malaria cases. Considering the drawback of EWARS, knowledge-based system in suggested. Instead of using data from the last few weeks, knowledge-based system collects all the necessary knowledge to detect potential malaria outbreak and help health authorities in decision-making toward malaria prevention and control. B. Knowledge-Based System Knowledge-based system can be defined as a computer application that analyzes and presents data to facilitate the decision-making process[14]. Many studies implemented knowledge-based system to improve health service. Integration of knowledge-based system and electronic health record database has been proposed to help diagnosis process of chronic disease [15]. Another study proposed knowledgebased system as a support in remote monitoring and consultation process in a hospital [16]. The implementation of knowledge-based system combines Artificial Intelligent (AI) techniques and a specific knowledge to imitate human behaviour in problem-solving process. KBS consists of a knowledge base and an inference engine. Knowledge base serves as a repository in which all the data, information and knowledge is stored. Meanwhile, the inference engine is where all the logical process happened. Knowledge base needs to be constantly updated to ensure that it is relevant to the current state. Knowledge can be updated by machine using machine learning[17]. Machine learning is a branch of artificial intelligence which enables a system to learn from data[7]. There are several machine learning approaches which commonly uses in learning process, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision tree, Bayesian.
Figure 1. Early Warning Alert and Response
EWARS reports support the health authorities in decision-making process regarding outbreak handling. EWARS is a traditional passive surveillance system which is highly dependent on primary health centers as the main actor in surveillance system. This kind of surveillance tends to expensive and inefficient. It causes a delay between data acquisition and dissemination of approximately 2 weeks[12] which can cause a disappointment of users [13]. Meanwhile, completeness and accuracy of the data play a vital role in early detection and response to malaria outbreak. Completeness and accuracy rate of EWARS report in Indonesia is less than 80%. It occurs due to several factors, including the lack of surveillance officers[6], the surveillance implementation has not fully involved hospitals and private clinics, the lack of data validation[5]. Most surveillance officers have multiple roles in primary health
Figure 2. Multilayer Perceptron Architecture[18]
Nowadays, ANN gains more attention due to its datadriven and self-adaptive nature[19]. ANN is widely recognized as a powerful approach for signal processing, classification and pattern recognition[18]. ANN has been applied in many problem domain, such as medical diseases diagnosis, environmental quality prediction and economic forecasting. There are several ANN-based models, such as
Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBFNN) dan Deep Neural network (DNN). MLP consists of 3 layers of perceptron which are interconnected by weights (figure 2). MLP uses Backpropagation algorithm to train the weights and sigmoid function as activation function in hidden layer[20]. Similar to MLP, RBFNN is also composed by 3 layers of neuron (figure 3). In contrast to MLP, RBFNN input layer is directly connected to hidden layer. In hidden layer, RBFNN uses a radial basis function as an activation function. A radial basis function is a multidimensional function that determines the distance between input vector and a predefined center vector[21]. The output layer is computed as a weighted sum of the hidden units. Many studies showed that RBFNN outperform MLP [22][23][24][25] due to its fast learning ability[18].
Deep Neural Network (DNN) is an ANN approach which has multiple layers of non-linear hidden units (figure 4) [26]. DNN is suggested to solve a high complexity problem. Despite the claim, some studies show that DNN did not result in better result than shallow neural network approach [27][28][29].
Figure 4. DNN Architecture[18]
Figure 3. RBFNN Architecture[18]
C. Geo-fencing Geo-fencing is defined as a technique that enables monitoring process in a specific geographic area which is surrounded by a virtual boundary [30]. Geo-fencing enables a system to trigger a specific event when a tracked mobile object entered or exited the area. Geo-fencing has been implemented in various field, such as logistic[30], children protection[31][32], medical care[33][34] and disaster mitigation[35][36].
Figure 5. Conceptual Model for Malaria Prevention and Control
In this study, geo-fencing technique is used to facilitate information and knowledge dissemination in malariaendemic areas. Geo-fencing will trigger an alert for objects who entered and exited an area with malaria outbreak or potential malaria outbreak to increase public awareness about the danger of malaria in that area. The system also provides the necessary knowledge to prevent the possibility of malaria spread.
technique is proposed to facilitate information and knowledge dissemination in malaria-endemic area to increase public awareness and knowledge about malaria. Finally, further research will be carried out to proven the conceptual model. It can be done by developing a prototype of knowledge-based system and performing a gap analysis to validate the effectiveness of knowledge-based system implementation.
III. PROPOSED MODEL In this section, a conceptual model for malaria prevention and control is proposed (figure 5). The model is composed of 3 subsystems, surveillance system, knowledge-based system and information dissemination system. The first part is surveillance system. The objective of surveillance system is to detect malaria trend by means of capturing malaria incidents in Indonesia. The surveillance system adapt the existing surveillance system in Indonesia. In this block, surveillance officers will collect malaria incident data from from midwives, private clinic and auxiliary health center. The data can also be obtained by doing active surveillance regularly. The collected data will be send to surveillance database using surveillance application weekly. In knowledge-based system block, the data gathered from surveillance database will be processed to gain knowledge. Machine learning technique is suggested as a means in knowledge-generating process. The complexity of data and hardware capability need to be considered in choosing machine learning technique. The knowledge gain from training process will be tested to ensure that the the model can give the accurate result. Afterward, the knowledge will be stored in knowledge base. The use of machine learning ensure that the learning process is done continuously. Thus, the knowledge in knowledge base is more relevant to the current state. The health authorities will request a relevant knowledge to help them in decision-making process regarding malaria prevention and control. The request is forwarded to inference engine and inference engine will collect the appropriate knowledge from knowledge base. Eventually, the inference engine conclude a result based on the collected knowledge. The last part in the conceptual model is information dissemination system. Information dissemination aims to increase public awareness about malaria in malaria-endemic area and provide the information to prevent malaria spread. The information generated from surveillance and knowledgebased system is will be disseminated among the people in a malaria-endemic area. Geo-fencing technique is used to determine information dissemination target by monitoring people whereabouts in malaria-endemic area.
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