International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 11, No. 2, 8 Mar. 2019
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Diabetes Prediction: A Deep Learning Approach
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Author(s)
Index Terms
Diabetes, Deep Neural Network (DNN), Prediction, Evaluation Metrics
Abstract
Nowadays, Diabetes is one of the most common and severe diseases in Bangladesh as well as all over the world. It is not only harmful to the blood but also causes different kinds of diseases like blindness, renal disease, kidney problem, heart diseases etc. that causes a lot of death per year. So, it badly needs to develop a system that can effectively diagnose the diabetes patients using medical details. We propose a strategy for the diagnosis of diabetes using deep neural network by training its attributes in five and ten-fold cross-validation fashion. The Pima Indian Diabetes (PID) data set is retrieved from the UCI machine learning repository database. The results on PID dataset demonstrate that deep learning approach design an auspicious system for the prediction of diabetes with prediction accuracy of 98.35%, F1 score of 98, and MCC of 97 for five-fold cross-validation. Additionally, accuracy of 97.11%, sensitivity of 96.25%, and specificity of 98.80% are obtained for ten-fold cross-validation. The experimental results exhibit that the proposed system provides promising results in case of five-fold cross-validation.
Cite This Paper
Safial Islam Ayon, Md. Milon Islam, "Diabetes Prediction: A Deep Learning Approach", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.2, pp. 21-27, 2019. DOI:10.5815/ijieeb.2019.02.03
Reference
[1]Diagnosis and Classification of Diabetes Mellitus, American Diabetes Association, Diabetes Care, vol. 33, Jan. 2010.
[2]R. Bellazzi, A. Abu-Hanna, “Data Mining Technologies for Blood Glucose and Diabetes Management,” Journal of Diabetes Science and Technology, vol. 3, pp. 603-612, May 2009.
[3]M. Panwar, A. Acharyya, R A. Shafik, D. Biswas, “K-Nearest Neighbor Based Methodology for Accurate Diagnosis of Diabetes Mellitus,” in Proc. Sixth International Symposium on Embedded Computing and System Design (ISED), pp. 132-136, 2016.
[4]Devi, M. Renuka, and J. Maria Shyla. "Analysis of Various Data Mining Techniques to Predict Diabetes Mellitus." International Journal of Applied Engineering Research 11.1, pp. 727-730, 2016.
[5]Goswami SK, Vishwanath M, Gangadarappa SK, Razdan R, Inamdar MN, “Efficacy of ellagic acid and sildenafil in diabetes-induced sexual dysfunction,” Pharmacogn Mag, vol. 10, 2014.
[6]Richard B. Balaban, “A Physician’s Guide to Talking About End-of-Life Care,” Journal of General Internal Medicine, vol. 15, pp. 195-200, Mar. 2017.
[7]Y. LeCun, Y. Bengio, G. E. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015.
[8]A. Thammano, A. Meengen, “A New Evolutionary Neural Network Classifier,” Springer-Verlag Berlin, pp. 249-255, (9), 2005.
[9]M. M. Islam, H. Iqbal, M. R. Haque, and M. K. Hasan, “Prediction of Breast Cancer using Support Vector Machine and K-Nearest Neighbors,” in Proc. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp 226-229, Dhaka, 2017.
[10]M. R. Haque, M. M. Islam, H. Iqbal, M. S. Reza, and M. K. Hasan, "Performance Evaluation of Random Forests and Artificial Neural Networks for the Classification of Liver Disorder," in Proc. International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, pp. 1-5, 2018.
[11]M. K. Hasan, M. M. Islam, and M. M. Hashem, “Mathematical model development to detect breast cancer using multigene genetic programming,” in Proc. 5th International Conference on Informatics Electronics and Vision (ICIEV), pp. 574-579, Dhaka, 2016.
[12]A. Kumar Dwivedi, “Analysis of computational intelligence techniques for diabetes mellitus prediction,” Neural Comput. Appl., vol. 13, no. 3, pp. 1–9, 2017.
[13]M. Heydari, M. Teimouri, Z. Heshmati, and S. M. Alavinia, "Comparison of various classification algorithms in the diagnosis of type diabetes in Iran," International Journal of Diabetes in Developing Countries, pp. 1-7, 2015.
[14]A. Ashiquzzaman, A. K. Tushar, M. Islam, J.-M. Kim et al., ``Reduction of overfitting in diabetes prediction using deep learning neural network,'' arXiv preprint arXiv:1707.08386, 2017.
[15]J. Zhu, Q. Xie, K. Zheng. “An Improved Early Detection Method of Type-2 Diabetes Mellitus Using Multiple Classifier Systems”. Information Sciences, volume 292, pages 1-14, 2015.
[16]M. Kumari, Dr. R. Vohra, and A. Arora, “Prediction of Diabetes using Bayesian Network,” International Journal of Computer Science and Information Technologies, vol. 5, pp. 5174-5178, 2014.
[17]T. Santhanam and M.S Padmavathi, “Application of K-Means and Genetic Algorithms for Dimension Reduction by Integrating SNM for Diabetes Diagnosis,” Procedia Computer Science, vol. 47, pp. 76-83, 2015.
[18]J. Vijayashree and J. Jayashree, “ An Expert System for the Diagnosis of Diabetic Patients using Deep Neural Networks and Recursive Feature Elimination,” International Journal of Civil Engineering and Technology, vol. 8, pp. 633-641, Dec. 2017.
[19]L. B. Goncalves and M. M. Bernardes, “Inverted Hierarchical Neuro-Fuzzy BSP System: A Novel Neuro-Fuzzy Model for Pattern Classification and Rule Extraction in Databases,” in IEEE Transactions on Systems, Man, and Cybernetics, vol. 36, no. 2, pp. 236-248, Mar. 2006.
[20]L. Han, S. Luo, H. Wang, L. Pan, X. Ma and T. Zhang, "An Intelligible Risk Stratification Model Based on Pairwise and Size Constrained Kmeans," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 5, pp. 1288-1296, Sept. 2017.
[21]Pima Indian Diabetes Data Set, [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes., accessed on May 01, 2018.
[22]Ahmad F, Isa NA, Hussain Z, and Osman MK, “Intelligent medical disease diagnosis using improved hybrid genetic algorithm--multilayer perceptron network,” Journal of Medical Systems, vol. 37, Apr. 2013.
[23]N. Yilmaz, O. Inan, and M. S. Uzer, “A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases,” Journal of Medical Systems, pp. 38-48, Apr. 2014.
[24]Ribeiro, U. Celeste, et al. “Diabetes classification using a redundancy reduction preprocessor,” Research on Biomedical Engineering, vol. 31, pp. 97-106, 2015.