2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 1 , Issue 2 , PP: 49-65, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
Nandini Gupta 1 * , Shubhangi Malik 2 , Hardik Chawla 3 , Surinder Kaur 4
Doi: https://doi.org/10.54216/FPA.010201
Diabetes nowadays is a familiar and long-term disease. If a prediction is made early, better treatment can be provided. The preprocessing data approach is extremely useful in predicting the disease at an early stage. "Many tools are used in determining significant characteristics such as selection, Prediction, and association rule mining for diabetes. The principal component analysis method was used to select significant attributes. Our judgments denote a strong association of diabetes with body mass indicator (BMI) and glucose degree. The study implemented logistic regression, decision trees, and ANN techniques to process Pima Indian diabetes datasets and predict whether people at risk have diabetes. It was analyzed that random forest had the best accuracy of 80.52 %. Out of 500 negative records & 268 positive records, our model correctly analyzed 403 records & 216 records, respectively.
Body Mass Indicator , Artificial Neural Network , Logistic Regression , Random Forest
[1] Temurtas, H., Yumusak, N., Temurtas, F., "A relative study on diabetes complain opinion using neural networks", Expert Syst, Vol. 36, pp. 8610 – 15, 2009.
[2] Chavey, A., Kroon, M., Bailbé, D., "programming of beta-cell diseases and intergenerational threat of type 2 diabetes Diabetes", Motherly Diabetes, Vol. 40, No. 5, pp. 323-30, 2014.
[3] Manzella,D., Grella,R., Abbatecola,AM., Paolisso,G.,"Repaglinide Administration Improves Brachial Reactivity in Type 2 Diabetic Cases", Diabetes Care, Vol. 28, pp. 366 –71, 2005.
[4] Mohamed,E.I., Linde, rR., Perriello,G., Di Daniele,N., Pöppl,S.J., De Lorenzo, A.," Predicting type 2 diabetes using an electronic nose ‐ grounded artificial neural network analysis", Diabetes nutrition & metabolismVol. 15, No. 4, pp.222-215, 2002.
[5] Volley, J.C., WilliamsG., (Eds.)., Textbook of diabetes, Blackwell Science, Oxford, 2003.
[6] Ahmadi KGuideline & book review. The internal (endocrine and lung). Ahmadi Cultural Institute, 2009.
[7] Morteza, Afsaneh, et al., "Inconsistency in albuminuria predictors in type 2 diabetes a comparison between neural network and tentative logistic retrogression", Translational
[8] Marateb, HamidR., et al."A cold-blooded intelligent system for diagnosing microalbuminuria in type 2", pp. 34-42, 2014.
[9] Torkestani, Javad, Akbari., and Elham, GhanaatPisheh., "A literacy automata- grounded blood glucose regulation medium in type 2 diabetes", Control Engineering Practice, Vol. 26, pp.151-159, 2014.
[10] Metz, CE., Wang, P-L., Kronman, HB., A new approach for testing the significance of differences between ROC angles measured from identified data. In DeconinckF. (editor) Information processing in medical imaging. The Hague Nijhoff, pp. 432-445, 1984.
[11] Nielsen, D., Krych, L., Burchard, K., "Beyond Genetics Influence of salutary factors and gut microbiota on type 1 diabetes", FEBS Lett, Vol. 588, pp. 4234 – 43, 2014.
[12] Pei, E., Li, J., Lu, C., Xu, J., Tang, T., Ye, M., et al," Goods of lipids and lipoproteins on diabetic bottom in people with type 2 diabetes mellitus a meta-analysis", J Diabetes Complications,Vol. 28,pp. 559 – 64, 2014.
[13] Livingstone, D., Totowa, NJ, Artificial Neural Networks Styles, and Operation. 1st ed Totowa, NJ Humana Press; 2008.
[14] Dunne, RA., Wiley, J., Inc, S., "A Statistical Approach to Neural Networks for Pattern Recognition", New Jersey John Wiley & Sons Inc; 2007.
[15] Zini,G., d'Onofrio,G.,"Neural network in hematopoietic malice", Clin Chim Acta, Vol. 333,No. 2,pp.195-201, 2003.
[16] Ruczinski,I., Kooperberg,C., etal., Logic Regresion. Journal of Computational and Graphical statistic, Vol. 12, No. 3, pp.475-511, 2003.
[17] Danesh-Pour, MS., Mehrabi, Y., Hedayati, M., Azizi, F., "Multivariable check of factors identified with the metabolic pattern using factor analysis (Persian)", Iranian Journal of Endocrinology and Metabolism, Vol. 30, pp.139-46, 2006.
[18] Talha Mahboob Alam, Muhammad Atif Iqbal, Yasir Ali, Abdul Wahab et al. "A model for early prediction of diabetes”, Informatics in Medicine Unlocked, 2019.
