[Legacy Report] (Technical Lecture) Advances in Intelligent Computational Methods for Decision Support

#Statistics; #Computational #Intelligence; #Decision #Support; #High #Dimensional #Data

Abstract:

Decision making from incomplete and high dimensional datasets is a challenging task, especially in the areas of medicine and public health management. In particular, the recent spread of infectious diseases such as Ebola, Swine flu and Dengue has showcased the inadequacies in evolving effective methods to contain them. The complexity lies in handling large number of people in short time, similarities of clinical symptoms and non-availability of resources (laboratoties and test equipment) that would cater to large population. A machine aided diagnosis based on clinical and laboratory features would be of great help in disease management. In this talk, we discuss new techniques based on statistics and computational intelligence to predict true positives cases more effectively [1], [2], [3], [4], [5]. Also, we showcase its performance with the state-of-the-art methods being employed in this area[6], [7], [8]. In addition, we present the challenges and approaches being employed by us in identifying influential features from these databases.

REFERENCES [1] M. Naresh Kumar Alternating Decision trees for early diagnosis of dengue fever, arXiv preprint, arXiv:1305.7331, 2013. [2] V. Sree Hari Rao and M. Naresh Kumar New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever, IEEE Transactions on Information Technology in Biomedicine, 16(1):112-118, 2012. [3] V. Sree Hari Rao and M. Naresh Kumar Estimation of the parameters of an infectious disease model using neural networks, Nonlinear Analysis: Real World Applications, 11 (3): 1810-1818, 2010. [4] V. Sree Hari Rao and M. Naresh Kumar Predictive Dynamics: Modeling for Virological Surveillance and Clinical Management of Dengue, Dynamic Models of Infectious Diseases, Volume 1, Springer, New York, USA, 2013. [5] V. Sree Hari Rao and M. Naresh Kumar, Control of Infectious Diseases: Dynamics and Informatics, Dynamic Models of Infectious Diseases, Volume 2, Springer, New York, USA, 2013. [6] D. Chadwick, B. Arch, A. Wilder-Smith, and N. Paton, Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: application of logistic regression analysis, J Clinical Virolology, 35(2):147153, 2006. [7] M. M. Ramos, K. M. Tomashek, D. F. Arguello, C. Luxemburger, L. Quiones, J. Lang, and J. L. Muoz-Jordan, Early clinical features of dengue infection in puerto rico, Transactions of the Royal Society of Tropical Medicine and Hygiene, 103 (9):878884, 2009. [8] L. Tanner, M. Schreiber, J. Low, A. Ong, and T. Tolfvenstam, Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness PLoS Neglected Tropical Diseases, 2 (3): 19, 2008.

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• Hyderabad, Andhra Pradesh
• India

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• Co-sponsored by Prof. Maniza Hijab, IT Department MJCET Hyderabad