Abstract
Understanding the propagation of human health behavior, such as smoking and obesity, and identification of the factors that control such phenomenon is an important area of research in recent years mainly because, in industrialized countries a substantial proportion of the mortality and quality of life is due to particular behavior patterns, and that these behavior patterns are modifiable. Predicting the individuals who are going to be overweight or obese in future, as overweight and obesity propagate over dynamic human interaction network, is an important problem in this area. The problem has received limited attention from the network analysis and machine learning perspective till date, though. In this work, we propose a scalable supervised prediction model based on convolutional regression framework that is particularly suitable for short time series data. We propose various schemes to model social influence for health behavior change. Further we study the contribution of the primary factors of overweight and obesity, like unhealthy diets, recent weight gains and inactivity in the prediction task. A thorough experiment shows the superiority of the proposed method over the state-of-the-art.
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References
Christensen, C., Albert, I., Grenfell, B., Albert, R.: Disease dynamics in a dynamic social networks. Phys. A 389(13), 2663–2674 (2010)
Read, J.M., Eames, K.T., Edmunds, W.J.: Dynamic social networks and the implications for the spread of infectious disease. J. Royal Soc. Interface 4(26), 1001–1007 (2008)
Caballero, B.: The global epidemic of obesity: an overview. Epidemiol. Rev. 29(1), 1–5 (2007)
Zagorsky, J.L.: Health and wealth: the late-20th century obesity epidemic in the US. Econ. Hum. Biol. 3(2), 296–313 (2005)
Dietz, W.H., Bellizzi, M.C.: Introduction: the use of body mass index to assess obesity in children. Am. J. Clin. Nutr. 70(1), 123s–125s (1999)
Poulain, M., Doucet, M., Major, G.C., Drapeau, V., Sériès, F., Boulet, L.P., Tremblay, A., Maltais, F.: The effect of obesity on chronic respiratory diseases: pathophysiology and therapeutic strategies. Can. Med. Assoc. J. 174(9), 1293–1299 (2006)
Derek Yach, D.S., Brownell, K.D.: Epidemiologic and economic consequences of the global epidemics of obesity and diabetes. Nature Med. 12, 62–66 (2006)
Chang, V.W., Christakis, N.A.: Medical modelling of obesity: a transition from action to experience in a 20th century American medical textbook. Sociol. Health Illn. 24(2), 151–177 (2002)
Eknoyan, G.: A history of obesity, or how what was good became ugly and then bad. Adv. Chronic Kidney Dis. 13(4), 421–427 (2006)
Shen, Y., Jin, R., Dou, D., Chowdhury, N., Sun, J., Piniewski, B., Kil, D.: Socialized Gaussian process model for human behavior prediction in a health social network. In: 2012 IEEE 12th International Conference on Data Mining, pp. 1110–1115, December 2012
Madan, A., Moturu, S.T., Lazer, D., Pentland, A.S.: Social sensing: obesity, unhealthy eating and exercise in face-to-face networks. In: Wireless Health, pp. 104–110 (2010)
Basu, S., Roy, S., Maulik, U.: Convolutional regression framework for health behavior prediction. In: 9th International Conference on Communication Systems and Networks (COMSNETS) (2017)
McPherson, M., Lovin, L.S., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Abdel Hamid, O., Mohamed, A.R., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22(10), 1533–1545 (2014)
Kim, Y.: Convolutional neural networks for sentence classification, CoRR, abs/1408.5882 (2014)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, pp. 807–814 (2010)
Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6354, pp. 92–101. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15825-4_10
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Madan, A., Cebrian, M., Moturu, S., Farrahi, K., Pentland, S.: Sensing the “health state” of a community. IEEE Pervasive Comput. 11(4), 36–45 (2012)
Hanneke, S., Fu, W., Xing, E.P.: Discrete temporal models of social networks. Electron. J. Stat. 4, 585–605 (2010)
Zaslavskiy, M., Bach, F., Vert, J.P.: A path following algorithm for the graph matching problem. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2227–2242 (2009)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357(4), 370–379 (2007)
Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. In: Proceedings of the National Academy of Sciences, vol. 106, no. 36, pp. 15274–15278 (2009)
Shen, Y., Phan, N., Xiao, X., Jin, R., Sun, J., Piniewski, B., Kil, D., Dou, D.: Dynamic socialized Gaussian process models for human behavior prediction in a health social network. In: Knowledge and Information Systems, pp. 1–25 (2015)
Phan, N., Dou, D., Piniewski, B., Kil, D.: Social restricted Boltzmann machine: human behavior prediction in health social networks. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 424–431 (2015)
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Basu, S., Roy, S., Maulik, U. (2017). Convolutional Regression Framework for Human Health Prediction Under Social Influences. In: Sastry, N., Chakraborty, S. (eds) Communication Systems and Networks. COMSNETS 2017. Lecture Notes in Computer Science(), vol 10340. Springer, Cham. https://doi.org/10.1007/978-3-319-67235-9_15
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DOI: https://doi.org/10.1007/978-3-319-67235-9_15
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