Exploratory study of Kohonen Network for human health state classification

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.

[1]  R. Sathya,et al.  Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification , 2013 .

[2]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[3]  Susanne Ebersbach,et al.  Artificial Neural Networks In Real Life Applications , 2016 .

[4]  Tai-hoon Kim,et al.  Use of Artificial Neural Network in Pattern Recognition , 2010 .

[5]  A. Lorenzo,et al.  The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations , 2001, European Journal of Clinical Nutrition.

[6]  A. G. Fisher,et al.  Generalized body composition prediction equations for men using simple measurement techniques , 1985 .

[7]  F. Tylavsky,et al.  BMI and an Anthropometry-Based Estimate of Fat Mass Percentage Are Both Valid Discriminators of Cardiometabolic Risk: A Comparison with DXA and Bioimpedance , 2013, Journal of obesity.

[8]  Tj Cole,et al.  Weight-stature indices to measure underweight, overweight and obesity , 1991 .

[9]  Aleksandar Kupusinac,et al.  Predicting body fat percentage based on gender, age and BMI by using artificial neural networks , 2014, Comput. Methods Programs Biomed..

[10]  G. Bray,et al.  Health hazards of obesity. , 1996, Endocrinology and metabolism clinics of North America.

[11]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[12]  Prachi Goyal,et al.  A Comprehensive Approach Towards Data Preprocessing Techniques & Association Rules , 2010 .

[13]  Roger W. Johnson.  Fitting Percentage of Body Fat to Simple Body Measurements: College Women , 1996, Journal of Statistics and Data Science Education.

[14]  Lambros Ekonomou,et al.  Greek long-term energy consumption prediction using artificial neural networks , 2010 .

[15]  K. K. Sahu,et al.  Normalization: A Preprocessing Stage , 2015, ArXiv.

[16]  Anastasios N. Venetsanopoulos,et al.  Artificial neural networks - learning algorithms, performance evaluation, and applications , 1992, The Kluwer international series in engineering and computer science.

[17]  Aleksandar Kupusinac,et al.  What kind of Relationship is Between Body Mass Index and Body Fat Percentage? , 2016, Journal of Medical Systems.

[18]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , 2006 .

[19]  Claude Bouchard,et al.  Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. , 1998, WMJ : official publication of the State Medical Society of Wisconsin.

[20]  S A Jebb,et al.  Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. , 2000, The American journal of clinical nutrition.

[21]  Nathalie Japkowicz,et al.  Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks , 2004, Machine Learning.

[22]  Shengdong Zhang,et al.  A novel ultrathin elevated channel low-temperature poly-Si TFT , 1999, IEEE Electron Device Letters.

[23]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[24]  Soumitra Dutta.  Knowledge Processing and Applied Artificial Intelligence , 1993 .