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Improved Adaptive Neuro-Fuzzy Inference System for HIV/AIDS Time Series Prediction

  • Conference paper
Informatics Engineering and Information Science (ICIEIS 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 253))

Abstract

Improving accuracy in time series prediction has always been a challenging task for researchers. Prediction of time series data in healthcare such as HIV/AIDS data assumes importance in healthcare management. Statistical techniques such as moving average (MA), weighted moving average (WMA) and autoregressive integrated moving average (ARIMA) models have limitations in handling the non-linear relationships among the data. Artificial intelligence (AI) techniques such as neural networks are considered to be better for prediction of non-linear data. In general, for complex healthcare data, it may be difficult to obtain high prediction accuracy rates using the statistical or AI models individually. To solve this problem, a hybrid model such as adaptive neuro-fuzzy inference system (ANFIS) is required. In this paper, we propose an improved ANFIS model to predict HIV/AIDS data. Using two statistical indicators, namely, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), the prediction accuracy of the proposed model is compared with the accuracies obtained with MA, WMA, ARIMA and Neural Network models based on HIV/AIDS data. The results indicate that the proposed model yields improvements as high as 87.84% compared to the other models.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Multimedia University, 63100, Cyberjaya, Malaysia

    Purwanto & C. Eswaran

  2. Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Malaysia

    R. Logeswaran

  3. Faculty of Computer Science, Dian Nuswantoro University, 50131, Semarang, Indonesia

    Purwanto

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  1. Purwanto
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  2. C. Eswaran
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  3. R. Logeswaran
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Editor information

Editors and Affiliations

  1. Advanced Informatics School (UTM AIS), UTM International Campus, 54100, Kuala Lumpur, Malaysia

    Azizah Abd Manaf , Shamsul Sahibuddin , Rabiah Ahmad  & Salwani Mohd Daud , ,  & 

  2. Information Systems Department, King Saud University, Riyadh, Saudi Arabia

    Eyas El-Qawasmeh

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© 2011 Springer-Verlag Berlin Heidelberg

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Purwanto, Eswaran, C., Logeswaran, R. (2011). Improved Adaptive Neuro-Fuzzy Inference System for HIV/AIDS Time Series Prediction. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25462-8_1

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  • DOI: https://doi.org/10.1007/978-3-642-25462-8_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25461-1

  • Online ISBN: 978-3-642-25462-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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