Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

A.B.M. Wijaya - Informatics Department, Universitas Kristen Immanuel, Jl. Solo Km 11.1, Sleman, 55571, Indonesia
D.S. Ikawahyuni - Informatics Department, Universitas Kristen Immanuel, Jl. Solo Km 11.1, Sleman, 55571, Indonesia
Rospita Gea - Informatics Department, Universitas Kristen Immanuel, Jl. Solo Km 11.1, Sleman, 55571, Indonesia
Febe Maedjaja - Informatics Department, Universitas Kristen Immanuel, Jl. Solo Km 11.1, Sleman, 55571, Indonesia


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DOI: http://dx.doi.org/10.30630/joiv.5.2.448

Abstract


Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm puts Restricted Boltzmann Machines (RBM) into a stacked process. The output of the first RBM will be the input for the next RBM. On the other hand, the NEAT algorithm works by investigating the neural network architecture and evaluating the architecture using a multi-layer perceptron algorithm. Collaboration with a Genetic Algorithm in NEAT is the key process in architecture development. The research results showed that DBN could be utilized as the initial weight for Backpropagation Neural Network at 22.61% on average. On the other hand, the NEAT algorithm could be used by collaborating with a multi-layer perceptron to solve this regression problem by providing 74.5% confidence. This work also reveals potential works in the future by combining the Backpropagation algorithm with NEAT as an evaluation function and by combining it with DBN algorithms to process the produced initial weight.

Keywords


NeuroEvolution; Deep Belief Network; Artificial Neural Network.

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References


International Diabetes Federation. IDF Diabetes Atlas. Brussels: International Diabetes Federation. 2019

T. Ligita,K Wicking, K Francis, et al. How people living with diabetes in Indonesia learn about their disease: A grounded theory study. PLOS ONE, p.1-9, 2009

Ni, X., Wang, H., Che, C., Hong, J. and Sun, Z.. Civil aviation safety evaluation based on deep belief network and principal component analysis. Safety science, 112, pp.90-95, 2019

La Cava, W. and Moore, J.H.. Learning feature spaces for regression with genetic programming. Genetic Programming and Evolvable Machines, p.1-35, 2020

Webb, G. I., Fürnkranz, J., Hinton, G., Sammut, C., and Patrick, J. Density-Based Clustering. Encyclopedia of Machine Learning, p.270–273. 2011

Zhang, Y. and Huang, G. Traffic flow prediction model based on deep belief network and genetic algorithm. IET Intelligent Transport Systems, 12(6), pp.533-541,2018

Alom, Z., Bontupalli, V., & Taha, Tarek M.. Intrusion Detection using Deep Belief Networks. IEEE. Vol. 15, p.339-344, 2015

Kaur, M. and Singh, D. Fusion of medical images using deep belief networks. Cluster Computing, p.1-15. 2019. https://doi.org/10.7717/peerj.3809

Sundararajan, S.K., Sankaragomathi, B. and Priya, D.S, Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images. Journal of medical systems, 43(6), p.174, 2019

Stanley, K.O dan R. Miikkulainen. Evolving Neural through Augmenting Topologies. Evolutionary Computation, 10(2): p.99-127.2002

Dua, D. and Graff, C, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], Irvine, CA: University of California, School of Information and Computer Science, 2019.

M. Pjanic, “The role of polycarbonate monomer bisphenol-A in insulin resistance,” PeerJ, vol. 5, September, 2017. https://doi.org/10.7717/peerj.3809.

Z. Punthakee, R. Goldenberg, and P. Katz, “Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome,” Canadian Journal of Diabetes, vol. 42, April, 2018. https://doi.org/10.1016/j.jcjd.2017.10.003

R. Nicoll and M. Y. Henein, “Caloric Restriction and Its Effect on Blood Pressure, Heart Rate Variability and Arterial Stiffness and Dilatation: A Review of the Evidence,” International Journal of Molecular Sciences, vol. 19, Issue 3, March, 2018. https://doi.org/10.3390/ijms19030751

A. Ruiz-Alejos, R. M. Carrillo-Larco. J. J. Miranda, R. H. Gilman, L. Smeeth, and A. Bernabe-Ortiz, “Skinfold thickness and the incidence of type 2 diabetes mellitus and hypertension: an analysis of the Peru Migrant study,” Public Health Nutrition, vol. 23, Issue 1, June, 2019. doi: 10.1017/S1368980019001307

Williams, L. dan Wilkins. Nurse’s Five-Minute Clinical Consult: Signs and Symptoms. Edisi Pertama. Wolters Kluwer. New York.F. 2008

NCD Risk Factor Collaboration (NCD-RisC) – Africa Working Group, Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies. International journal of epidemiology, (46), pp.1421-1432 , 2017

Wadhwa, S. and Babber, K., “Artificial Intelligence in Health Care: Predictive Analysis on Diabetes Using Machine Learning Algorithms”, International Conference on Computational Science and Its Applications, vol. 12250, 2020. https://doi.org/10.1007/978-3-030-58802-1_26

Vuvor, F. and Egbi, B., “Correlation of diabetes mellitus and body weight of adults above the age of 30 years in a medical facility in Ghana”, Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 11, 2017.

G.E Hinton, S. Osindero, & Y.W Teh. A fast learning algorithm for deep belief nets. Neural Computation, (18), p.1527 – 1554, 2006

S. Kamada , T. Ichimura, and T. Harada. Knowledge extraction of adaptive structural learning of deep belief network for medical examination data. International Journal of Semantic Computing, 13(01), pp.67-86, 2019

K.P. Lin, P.F Pai, and Y.J. Ting, Deep belief networks with genetic algorithms in forecasting wind speed. IEEE Access, 7, pp.99244-99253, 2019

Xu, H. and Jiang, C. Deep belief network-based support vector regression method for traffic flow forecasting. Neural Computing and Applications, pp.1-10, 2019

Ouyang, T., He, Y., Li, H., Sun, Z. and Baek, S. Modeling and forecasting short-term power load with copula model and deep belief network. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(2), pp.127-136, 2019

Khatami A, Khosravi A, Nguyen T, Lim CP, Nahavandi S., Medical image analysis using wavelet transform and deep belief networks Expert Systems with Applications, Elsevier vol 86, pp 190-8, 2017,

Movahedi F, Coyle JL, Sejdić E., Deep belief networks for electroencephalography: A review of recent contributions and future outlooks , IEEE journal of biomedical and health informatics, vol 22(3):pp 642-52, 2017

Movahedi F, Coyle JL, Sejdić E., Deep belief networks for electroencephalography: A review of recent contributions and future outlooks, IEEE journal of biomedical and health informatics, Vol 22(3): pp 642-52, 2017.

R. Jiao, X. Huang, X. Ma, L. Han and W. Tian, "A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-Term Wind Power Forecasting," IEEE Access, vol. 6, pp. 17851-17858, 2018, doi: 10.1109/ACCESS.2018.2818108

Khandelwal, Renu. Deep Learning – Deep Belief Network (DBN). Data Driven Investor. Available: https://medium.com/datadriveninvestor/deep-learning-deep-belief-network-dbn-ab715b5b8afc.2018

Belhaj Slimene, S. and Mamoghli, C. NeuroEvolution of Augmenting Topologies for predicting financial distress: A multicriteria decision analysis. Journal of Multi‐Criteria Decision Analysis, 26(5-6), p. 320-328, 2019

H. Turabieh. Comparison of NEAT and Backpropagation Neural Network on Breast Cancer Diagnosis. International Journal of Computer Applications. 139(8): p.40-44. 2016.

M. E. Yuksel, Advanced Engineering Informatics Agent-based Evacuation Modeling with Multiple Exits using NeuroEvolution of Augmenting Topologies. Advanced Engineering Informatics, (35): p.30–55, 2018.




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