Predictive Intelligent Decision Support Model in Forecasting of the Diabetes Pandemic Using a Reinforcement Deep Learning Approach

Full Text (PDF, 541KB), PP.40-48

Views: 0 Downloads: 0


Arnold Adimabua Ojugo 1,* Elohor Ekurume 2

1. Department of Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria

2. Department of Computer Science, Delta State University, Abraka, Delta State, Nigeria

* Corresponding author.


Received: 11 Dec. 2020 / Revised: 4 Jan. 2021 / Accepted: 26 Jan. 2021 / Published: 8 Apr. 2021

Index Terms

Diabetes, Type-I, Type-II, Gestational, deep neural network, modular learning, Silent killer


Diabetes has since become global pandemic – which must be diagnosed early enough if the patients are to survive a while longer. Traditional means of detection has its limitations and defects. The adoption of data mining tools and adaptation of machine intelligence is to yield an approach of predictive diagnosis that offers solution to task, which traditional means do not proffer low-cost-effective results. The significance thus, is to investigate data feats rippled with ambiguities and noise as well as simulate model tractability in order to yield a low-cost and robust solution. Thus, we explore a deep learning ensemble for detection of diabetes as a decision support. Model achieved a 95-percent accuracy, with a sensitivity of 0.98. It also agrees with other studies that age, obesity, environ-conditions and family relation to the first/second degrees are critical factors to be watched for type-I and type-II management. While, mothers with/without previous case of gestational diabetes is confirmed if there is: (a) history of babies with weight above 4.5kg at birth, (b) resistant to insulin showing polycystic ovary syndrome, and (c) have abnormal tolerance to insulin.

Cite This Paper

Arnold Adimabua Ojugo, Elohor Ekurume, " Predictive Intelligent Decision Support Model in Forecasting of the Diabetes Pandemic Using a Reinforcement Deep Learning Approach", International Journal of Education and Management Engineering (IJEME), Vol.11, No.2, pp. 40-48, 2021. DOI: 10.5815/ijeme.2021.02.05


[1]A.A. Ojugo., D.O. Otakore., Improved early detection of gestational diabetes via intelligent classification models: a case of Niger Delta region of Nigeria, J. of Computer Science & Application, 6(2): pp82-90, doi: 10.12691/jcsa-6-2-5, 2018

[2]A.A. Ojugo., A. Eboka., R.E. Yoro., M. Yerokun., F.N. Efozia., Hybrid model for early diabetes diagnosis, Maths. and Computers in Science & Industry, 50, pp207-217, 2015, [web]

[3]M. I. Harris., Diabetes in America: Epidemiology and scope of the problem. Diabetes Care, 21(3), pp. 11-14, 1998 

[4]A.C. Menezes., P.R. Pinheiro., M.C. Pinheiro., T. Pequeno., Towards applied hybrid model in decision making: support the early diagnosis of type 2 diabetes, In Proc of Maths & Computer in Science and Engineering, doi: 10.1007/978-3-642-34062-8_84

[5]D.M. Holmes, The person and diabetes psychosocial context. Diabetes Care, 9(2), pp.194-206, 1986.

[6]The Expert Committee., On the diagnosis and classification of diabetes mellitus. Report of the Expert Committee on the diagnosis and classification of diabetes mellitus. Diabetes Care, 20, pp. 1183-1197, 1997

[7]American Diabetes Association. Standards of Medical Care in Diabetes – 2009. Diabetes Care, 32: S13-61.

[8]N.H. Barakat, A.P. Bradley, M.N.H Barakat, Intelligible support vector machines for diagnosis of diabetes mellitus, IEEE Transactions on Information Technology in Biomedicine, 14(4), pp 1114-1120, 2010

[9]Canadian Diabetes Association. Standards of Medical Care in Diabetes 2014, Journal of Diabetes Care, 32, S13 – 16.

[10]G. Berks, D. Keyserlingk, J. Jantzeen., et al., Fuzzy clustering: versatile means to explore medical database, ESIT, Aachen, Germany, 2000

[11]S. Chinenye, E. Young, State of diabetes care in Nigeria: a review, The Nigerian Health Journal, 11(4), pp101-106, 2011.

[12]R. Goldenberg, Z. Punthakee,, Definition, classification and diagnosis, prediabetes and metabolic syndrome, 37(1), S8-S11, 2013

[13]A. Edo, G.O. Edo, O.A. Ohehen, N.P. Ekhator, W.C. Ordiah, Age and diagnosis of type-2 diabetes seen in Benin City Nigeria, African J. Diabetes Medicine, 23(1), pp18, 2015

[14]M. Khashei, S. Eftekhari, J. Parvizian, Diagnosing diabetes type-II using a soft intelligent binary classifier model, Review of Bioinformatics and Biometrics, 1(1), pp 9 – 23, 2012

[15]O. Vaarala, M. Knip, J. Paronen, A.M. Hamalainen, P. Muona, P., Vaatainen, M., Ilonen, J., Simell, O and Akerblom, H.K., Cow’s milk formula feeding induces primary immunization to insulin in infants at genetic risk for type-1 diabetes, Diabetes, 8(7), pp1389-1394, 1999

[16]A.A. Ojugo., O.D. Otakore., Empirical evaluation for intelligent predictive models in the prediction of potential cancer problematic cases in Nigeria, Journal of Mathematical and Computational Science, 2020

[17]A.A. Ojugo., A.O. Eboka., Modelling behavioural evolution as social predictor for coronavirus contagion and immunization in Nigeria, J. Applied Sci., Engr., Tech. & Education, 3(2): pp37–45, 2021, doi: 10.35877/454RI.asci130

[18]A.A. Ojugo., O.D. Otakore., Forging an optimized Bayesian network model with selected parameter for detection of Coronavirus in Delta State, J. Appl. Sci., Engr., Tech. & Edu., 3(1): pp37–45, 2020,  doi: 10.35877/454RI.asci2163

[19]R.E. Yoro., A.A. Ojugo., Quest for prevalence rate of Hepatitis-B infection in Nigeria: comparative study of supervised versus unsupervised model, American Journal of Modeling & Optimization, 7(2): 42-48, doi: 10.12691/ajmo-7-2-2, 2019 

[20]A.A. Ojugo., I.P. Okobah., Prevalence rate of hepatitis-B virus infection in Niger Delta region of Nigeria using graph-based diffusion heuristic model, IJCAOnline Int. Journal of Computer Application, 179(39): pp27 –33, 2018

[21]A.A. Ojugo., D.A. Oyemade., D. Allenotor., R.E. Yoro., C.N. Anujeonye., Immunization problem for Ebola virus in rural Sierra-Leone, African J. of Comp. & ICT., 8(1): pp1–10, 2015

[22]A.A. Ojugo., F.O. Aghware., R.E. Yoro., M.O. Yerokun., A.O. Eboka., C.N. Anujeonye., F. Efozia., Predict behavioral evolution on graph model, Adv. in Net., 3(2): pp8-21, 2015.

[23]A.A. Ojugo., J. Emudianughe., R.E. Yoro., E. Okonta., A.O Eboka., A hybrid neural network gravitational search algorithm for rainfall runoff modeling and simulation in hydrology, Progress in Intelligence Computing and Applications, 2(1): 22-33, doi: 10.4156/pica.vol2.issue1.2, 2013

[24]A.A. Ojugo., R.E. Yoro., Computational intelligence in stochastic solution for Toroidal N-queen, Progress in Intelligence Computing and Applications, 2(1): 46-56, 2013

[25]B. Ghazale, Reasoning using modular neural network – innovative solution to address question answering AI tasks, retrieved from [web]:, July 18, 2020

[26]R.E. Yoro., Machine learning optimization model for network intrusion detection, Unpublished Doctoral thesis submitted to Babcock University Ilishan-Remo, Ogun State, Nigeria, November 2020

[27]A.A. Ojugo., R.E. Yoro., Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack, Int. Journal of Electrical and Computer Engineering, 11(2): pp1498-1509, doi: 10.11591/ijece.v11i2.pp1498-1509, 2021

[28]A.A. Ojugo., E. Ekurume., Towards a more satisfied user framework through a dependable-secured hybrid deep learning ensemble for detection of credit-card fraud, Submitted to Int. J. of Emerging Trends in Engineering Research, 2020

[29]A.A. Ojugo., E. Ekurume., Spatio-temporal solution for credit-card fraud using a genetic algorithm trained modular neural network ensemble, Submitted to Int. J. of Emerging Trends in Engineering Research, 2020

[30]A.A. Ojugo., A. Eboka., E. Okonta., R.E. Yoro., F.O Aghware., Genetic algorithm rule-based intrusion detection system, J. Emerging Trends in Comp. Info. Sys., 3(8): pp1182-1194, 2012

[31]M. Perez, T. Marwala, Stochastic optimization approaches for solving Sudoku, IEEE Transaction on Evol. Comp., pp.256–279, 2011

[32]R. Reynolds, Introduction to cultural algorithms, Transaction on Evolutionary Programming, pp.131-139, 1994.

[33]R. Ursem, T. Krink, M. Jensen, Z. Michalewicz, Analysis and modeling of controls in dynamic systems. Transaction on Memetic Systems and Evolutionary Computing, 6(4), pp.378-389, 2002

[34]A.A. Ojugo, A.O., Eboka, Modeling solution of market basket associative rule mining approaches using deep neural network, Digital Technologies, 3(1): pp1–8, 2018, doi: 10.12691/dt-3-1-1, [web]:

[35]Y. Bengio., A. Courville, P. Vincent., Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence , 35 (8), pp1798-1828, 2013.

[36]G. Hinton, L. Deng, D. Yu, G.E. Dahl, A.R. Mohamed et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag., Vol. 29, pp82–97, 2012.

[37]X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, In Proc. of 13th Int Conf. on Artificial Intelligence and Statistics, Sardinia, Italy, 13–15 May 2010; pp. 249–256.

[38]D. Erhan, Y. Bengio, A. Courville, P.A. Manzagol, P. Vincent, S. Bengio, Why does unsupervised pre-training help deep learning?, Journal of Machine Learning Res., Vol. 11, pp625–660, 2010

[39]A.A. Ojugo., R.E. Yoro., Empirical solution for an optimized machine learning framework for anomaly-based network intrusion detection, Technology Report of Kansai University, TRKU-13-08-2020-10996, 62(10): pp6353-6364, 2020

[40]D.A. Oyemade., A.A. Ojugo., A property oriented pandemic surviving trading model, Int. J. Advanced Trends in Computer Science and Engineering, 9(5): pp7397-7404, 2020

[41]A.A. Ojugo., A. Eboka., Empirical evaluation on comparative study of machine learning techniques in detection of distributed denial of service attack, J. Applied Sci. Eng. Tech. & Edu., 2(1): pp18–27, 2020, doi: 10.35877/454RI.asci2192

[42]A.A. Ojugo., D.A. Oyemade., Predicting diffusion dynamics of coronavirus in Nigeria through ties-strength threshold on a cascading SI-graph, Technology Report of Kansai University, TRKU-13-08-2020-10998, 62(8): pp4313-4323, 2020

[43]A.A. Ojugo., A.O. Eboka., Signature-based malware detection using approximate Boyer Moore string matching algorithm, International Journal of Mathematical Sciences and Computing(IJMSC), 5(3): pp49-62, doi: 10.5815/ijmsc.2019.03.05, 2019

[44]R.E. Yoro., A.A. Ojugo., Quest for prevalence rate of Hepatitis-B infection in Nigeria: comparative study of supervised versus unsupervised model, American Journal of Modeling & Optimization, 7(2): 42-48, doi: 10.12691/ajmo-7-2-2, 2019

[45]A.A. Ojugo., E. Ben-Iwhiwhu, et al., Malware propagation on social time varying networks: comparative study of machine learning frameworks, International Journal of Modern Education and Computer Science (IJMECS), 6(8): pp25-33, doi: 10.5815/ijmecs.2014.08.04, 2014