Exploratory Study of Kohonen Network for Human Health State Classification

Hamijah Rahman - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Nureize Arbaiy - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Muhammad Che Lah - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Norlida Hassan - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.2.3-2.143

Abstract


Kohonen Network is an unsupervised learning which forms clusters from patterns that share common features and group similar patterns together. This network are commonly uses grids of artificial neurons which connected to all the inputs. This paper presents an exploratory study of Kohonen Neural Network to classify human health state. Neural Connection tool is used to generate the result based on Kohonen learning algorithm. Procedural steps are provided to assist the implementation of the Kohonen Network. The result shows that side 2 is more appropriate for this problem with efficient learning rate 1.0. It gives good distribution for training and test patterns. Study to the variation of dataset’s size will be considered in the near future to evaluate the performance of the network.

Keywords


Clustering; Kohonen Neural Network; Body fat; Health state

Full Text:

PDF

References


S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, “A novel ultrathin elevated channel low-temperature poly-Si TFT,†IEEE Electron Device Lett., vol. 20, pp. 569–571, Nov. 1999.

L. Ekonomou, “Greek long-term energy consumption prediction using artificial neural networks,†Energy, vol. 35(2), pp. 512-517, 2010.

E. Guresen, G. Kayakutlu, and T. U. Daim, “Using artificial neural network models in stock market index prediction,†Expert Systems with Applications, vol. 38(8), pp. 10389-10397, 2011.

S. A. Kalogirou, “Artificial neural networks in renewable energy systems applications: a review,†Renewable and sustainable energy reviews, 5(4), 373-401, 2001.

S. Dutta, Knowledge processing and applied artificial intelligence. Elsevier, 2014.

J. K. Basu, D. Bhattacharyya and T.H. Kim, “Use of artificial neural network in pattern recognition,†International journal of software engineering and its applications, vol. 4(2), pp. 23 – 34, 2010.

S. Samarasinghe, “Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition,†CRC Press, 2016.

R. Sathya and A. Abraham, “Comparison of supervised and unsupervised learning algorithms for pattern classification,†International Journal of Advanced Research in Artificial Intelligence, vol. 2(2), pp. 34-38, 2013.

N. Japkowicz, “Supervised versus unsupervised binary-learning by feedforward neural networks,†Machine Learning, vol. 42(1-2), pp. 97-122, 2001.

World Health Organization. The World Health Report 2001: Mental health: new understanding, new hope. World Health Organization, 2001.

GA., Bray “Health hazards of obesity,†Endocrinology and Metabolism Clinics, vol. 25 (4), pp. 907–919, 1996.

T. J. Cole, “Weight-stature indices to measure underweight, overweight, and obesity,†Anthropometric assessment of nutritional status. 1991.

National Heart, Lung, Blood Institute, National Institute of Diabetes, Digestive, & Kidney Diseases (US). (1998). Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report (No. 98). National Heart, Lung, and Blood Institute.

A., Kupusinac, E., Stokić, E., Sukić, O., Rankov, & A. Katić, “What kind of relationship is between body mass index and body fat percentage?,†Journal Of Medical Systems, 41(1), 5, 2017.

A., Kupusinac, E., Stokić, & R. DoroslovaÄki. “Predicting body fat percentage based on gender, age and BMI by using artificial neural networks,†Computer Methods And Programs In Biomedicine, vol. 113(2), pp. 610-619, 2014.

B., Krachler, E., Völgyi, K., Savonen, F. A., Tylavsky, M., Alén, & S. Cheng. “BMI and an anthropometry-based estimate of fat mass percentage are both valid discriminators of cardiometabolic risk: a comparison with DXA and bioimpedance.†Journal of Obesity, pp. 1-14, 2013.

World Health Organization, World Organization of National Colleges, Academies, & Academic Associations of General Practitioners/Family Physicians. Integrating mental health into primary care: a global perspective. World Health Organization, 2008.

D. Gallagher, S.B. Heymsfield, M. Heo, S.A. Jebb, P.R. Murgatroyd, and Y. Sakamoto, “Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index,†The American journal of clinical nutrition, vol. 72(3), pp. 694-701, 2000.

P. Deurenberg, A. Andreoli, P. Borg, K. Kukkonen-Harjula, A. De Lorenzo, W.D. van Marken Lichtenbelt, and N. Vollaard, “The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations. European journal of clinical nutrition, vol. 55(11), pp. 973, 2001.

T. Kohonen, S. Kaski, K. Lagus, J. Salojarvi, J., Honkela, V. Paatero, and A. Saarela, “Self-organization of a massive document collection,†IEEE transactions on neural networks, 11(3), 574-585, 2000.

R. W. Johnson, “Fitting percentage of body fat to simple body measurements,†Journal of Statistics Education, vol. 4(1)1996.

J. R. Rabuñal, (Ed.). Artificial neural networks in real-life applications. IGI Global, 2005.

C. Bailey, The New Fit or Fat, Boston: Houghton-Mifflin, 1991.

J. S. Malik, P. Goyal, and A.K. Sharma, “A comprehensive approach towards data preprocessing techniques & association rules,†In Proceedings of the 4th National Conference, 2010.

S. Patro, and K.K. Sahu, Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462, 2015.

N. Karayiannis, and A. N. Venetsanopoulos, “Artificial neural networks: learning algorithms, performance evaluation, and applications,†vol. 209. Springer Science & Business Media, 2013.

S. S. Haykin, S. S. Haykin, S. S. Haykin, and S. S. Haykin. Neural networks and learning machines, vol. 3. Upper Saddle River, NJ, USA: Pearson, 2009.

K. W. Penrose, A. G. Nelson, and A. G. Fisher, “Generalized body composition prediction equation for men using simple measurement techniques,†Medicine & Science in Sports & Exercise, vol.17(2), 189, 1985.