Department of Information and Communication Engineering (ICE)

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Browsing Department of Information and Communication Engineering (ICE) by Author "JONY AKTER"

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    PREDICTION OF DENGUE OUTBREAKS IN PERSPECT OF BANGLADESH USING ARTIFICIAL INTELLIGENCE
    (2021-03-20) JONY AKTER
    Dengue Fever is a disease that has grown worldwide in the last few years. The information about the patients can be maintained with clinical documents. By keeping huge volume of clinical documents we can easily predict the occurrence of dengue disease in the patients. Dengue is considered to be one of the vital diseases which are spreading in more than 110 countries. It is a vector borne disease caused by the mosquitoes of female Aedes Albopictus and Aedes Aegypti which are well suited human environment. With nearly 45,000 cases reported all over Bangladesh in the last year. Dengue fever has become a major health hazard in Bangladesh over the past few years. Several studies show that which variable is related to the disease, however, as far as we know there is no effective study in Bangladesh that reveals this relation. This research shows the accuracy of different algorithms of Artificial Neural Network (ANN) to predict Dengue outbreaks. Firstly, data is collected from different hospitals, and then data is normalized. Data is analyzed to see the infection rate of different parameters. Then data is splitted for training and testing and finally find the best accuracy for different Multi-layer perceptron algorithms such as Scaled Conjugate Gradient (SCG), Levenberg–Marquardt (LM) based on back propagation algorithm and Learning Vector Quantization (LVQ). In this work, an analysis of the influence of variables is performed and shows the efficiency of the neural networks is used to predict the number of disease cases. Accuracy of SCG, LM and LVQ algorithm is 87.1%, 95% and 90.3% with MSE 0.137, 0.0241 and 0.0967 respectively. And decision tree algorithm gives 98.92% in training stage but 90% accuracy gives in validation and testing. LM outperforms with minimum MSE than other algorithm. Thus, this work finds an efficient prediction model for dengue cases for districts of Bangladesh.