5 th International Advanced Technologies Symposium (IATS’09), May 13-15, 2009, Karabuk, Turkey © IATS’09, Karabük University, Karabük, Turkey RECOGNITION OF DENGUE DISEASE PATTERNS USING ARTIFICIAL NEURAL NETWORKS B. Gultekin Cetiner a , Murat Sari b and Hani M. Aburas c a, * Faculty of Engineering, Department of Manufacturing and Materials Engineering, IIUM, Kuala Lumpur 50728, Malaysia. E-mail: drcetiner@iiu.edu.my b Pamukkale University, Faculty of Art and Science, Department of Mathematics, 20020, Denizli, Turkey E-mail: msari@pau.edu.tr c Faculty of Engineering, Department of Industrial Engineering, KAU, P.O. Box 80204, Jeddah 21589, SA E-mail: haburas@kau.edu.sa Abstract This research aimed at the recognition of the patterns for dengue disease patterns using Artificial Neural Networks (ANN’s). Real data was provided by Singaporean National Environment Agency (NEA), for academic purposes only. Obtained data was used to model the behavior of dengue cases based on the physical parameters of mean temperature, mean relative humidity and total rainfall. The set of data recorded weekly consists of dengue reported confirmed cases together with three aforementioned parameters and for a six-year period, January 2001 to April 2007. Keywords: Disease Pattern Recognition, Artificial Neural Networks 1. Introduction Dengue is an infectious disease transmitted from person to person by a mosquito, Aedes aegypti, which is a major vector for the virus in different parts of the globe; Ae. albopictus is also considered as a secondary vector, Fig 1. The first recorded epidemics of dengue like disease occurred in 1635 in French West Indies, Batavia (Jakarta), and Cairo [1, 2, 3]. Dengue out-breaks have also been reported from different parts of the world in the past two centuries. Over the past two decades, there has been a dramatic increase in the Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS) epidemics in South East Asian countries. World Health Organization (WHO) estimated that about 50-100 million cases of dengue are recorded from all over the world annually, and two fifth of the world population is at risk and more than one hundred countries have been affected by dengue or DHF/DSS epidemics. Since 1950, more than 500,000 hospitalized cases and approximately 70,000 deaths of the children have been recorded; infection rate among the children is as high as 64 per 1000 population [2]. Dengue is characterized by high fever, headache, pain in various parts of the body, prostration, rash lymphodenopathy, and leucopenia [1]-[4]. DHF is a severe febrile disease characterized by abnormalities of homeostasis and increased vascular permeability which may result into DSS [5]-[6]. Normally, cycles of dengue virus is transmitted from human-to-human by mosquito bites. Several species of mammals and lower primates act as reservoir of the dengue virus [7]. From feeding on an infected viraemic human, the female Aedes mosquito is able to transmit dengue virus after an extrinsic incubation period of 8-10 days [8]. Aedes mosquito rests inaccessible areas behind the human dwellings; hence, the collection of these mosquitoes by hand catch is very difficult. However, the adult mosquitoes are being collected by either man- biting/landing or netting. These methods are considered as unethical issues for measuring the adult population. Therefore, attempts are being made to collect these mosquitoes through different types of traps developed by different companies. In this study, various types of traps have been tested to choose the most efficient one for the collection of Aedes population. As per our study, Black Hole traps was considered as the most efficient traps, which was used in different parts of the study areas. Figure 1: Dengue Transmitting Vector World Wide (Source:www.broad.mit.edu/news/links/dengue- 08172005.html , 2005) The main purpose of this study is to investigate the feasibility of applying the Artificial Neural Networks (ANN’s) technique to recognize dengue disease patterns based on the measured real parameters: mean temperature, mean relative humidity, total rainfall and reported dengue cases as corresponding output to those three mentioned parameters. ANN’s have been used herein due to their ability to learn from given examples which makes them perfect tools since there is no need to model each individual case mathematically. In this paper, we use Artificial Neural Networks to recognize the number of dengue-confirmed cases. The real data obtained from the Singaporean National Environment Agency (NEA) has been adopted to model the behavior of dengue confirmed cases based on the parameters of mean temperature, mean relative humidity and total rainfall. The set of data recorded weekly consists