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IJSTR >> Volume 7 - Issue 8, August 2018 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Feed-Forward Neural Network Model For The Accurate Prediction Of Diabetes Mellitus

[Full Text]

 

AUTHOR(S)

Yinghui Zhang, Zihan Lin, Yubeen Kang, Ruoci Ning, Yuqi Meng

 

KEYWORDS

ANN, Diabetes, feed forward network, Levenberg-Marquardt training, Matlab, Neural Networks, Prediction of Diabetes.

 

ABSTRACT

Diabetes mellitus is a group of metabolic diseases showing high blood sugar levels over prolonged periods. It is one of the deadly diseases growing at rapid rates in developing countries. Diabetes has affected over 246 million people worldwide. According to the World Health Organization (WHO) report, this number is expected to rise to over 380 million by 2025. If untreated, diabetes can lead to long-term complications such as heart disease and kidney failure. Therefore, there is a great need for the timely diagnosis of diabetes for people around the world. In particular, diabetes has been identified to be a very serious threat to younger generations and working individuals. Diabetes can be managed if it can be predicted during the early stages with changes in the diet and lifestyle of the patient. Therefore, this paper proposes a model for the early prediction of diabetes by considering major risk factors. An artificial neural network model with the Levenberg-Marquardt training algorithm is built using the PIMA Indian Diabetes dataset. The objective of the study is to predict the occurrence of diabetes mellitus using known risk factors based on feed-forward artificial neural network.

 

REFERENCES

[1] Ryde ́n L, Standl E, Bartnik M, Van den Berghe G, Betteridge J, De Boer MJ, et al. Guidelines on diabetes, pre-diabetes, and cardiovascular diseases: full text. European Heart Journal Supplements. 2007; 9 (suppl C):C3–C74. https://doi.org/10.1093/eurheartj/ehl261
[2] International Diabetes Federation, http://www.diabetesatlas.org.
[3] Cade WT. Diabetes-related microvascular and macrovascular diseases in the physical therapy setting. Phys Ther Nov 2008;88(11):1322–35.
[4] Habibi S, Ahmadi M, Alizadeh S. Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining. Global journal of health science. 2015; 7(5):304. https://doi.org/10.5539/gjhs.v7n5p304 PMID: 26156928
[5] Krentz AJ, Bailey CJ. Oral antidiabetic agents: current role in type 2 diabetes mellitus. Drugs 2005;65(3):385–411.
[6] Alghamdi, Manal, et al. "Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford Exercise Testing (FIT) project." PloS one 12.7 (2017): e0179805.
[7] Sushant Ramesh, et al. “A Deep Learning Approach to Identify Diabetes”, Advanced Science and Technology Letter Vol. 145 (NGCIT 2017): Pp. 44-49
[8] BaratamYasaswi and BodapatiPrajna, “The Early Augmentation for Diabetes Diagnosis Using Data Mining Approaches”, IJCST Vol. 7, Issue 3, July-Sept 2016: pp 27-31.
[9] Sushant Ramesh et al. “Optimal Predictive Analytics of Pima Diabetics Using Deep Learning”, International Journal of Database Theory and Applicaation. Vol.10, No.9 (2017) pp. 47-62.
[10] Kamble and Patil, “Diabetes Detecting using Deep Learning Approach”, International Journal for Innovative Research in Science & Technology, Vol.2, Issue.12, May 2016. Pp. 342-349
[11] Veena Vijayan and Anjali, “Prediction and Diagnosis of Diabetes Mellitus – A Machine Learning Approach”, IEEE Recent Advances in Intelligent Computational Systems (RAICS), Dec-2015, pp: 122-127.
[12] Radha & Srinivasan, “Predicting Diabetes by Consequencing the various Data Mining Classification Techniques”, International Journal of Innovative Science, Engineering and Technology, Vol.1, Issue.6,Aug-2014.
[13] Ayush Anand., Divya Shakti.: Prediction of Diabetes Based on Personal Lifestyle Indicators.: IEEE 1st Intl. Conf. on Next Generation Computing Technologies (NGCT), Dehradun, pp. 673-676. (2015).
[14] Durairaj., Kalaiselvi.: Prediction of Diabetes Using Back Propagation Algorithm.: Intl. Journal of Emerging Technology and Innovative Eng., Vol.1, Issue 8, pp.21-25, (2015)
[15] Sapna S., Pravin Kumar M.: Diagnosis of Disease from Clinical Big Data Using Neural Network.: Indian Journal of Science and Technology, Vol 8(24)., pp. 1-7., (2015).
[16] ZhilbertTafa., NerxhivanePervetical., BertranKarahoda.: An Intelligent System for Diabetes Prediction: 4th Mediterranean Conf. on Embedded Computing.pp. 378-382. (2015).