Abstract
Diabetes is one among many chronic diseases. It is the most common disease and lots of peoples are affected by this. There are many things that are liable for diabetes, mainly age, obesity, weakness, sudden weight loss, and many more. Diabetes patients have high risk of diseases like cardiopathy, renal disorder, stroke, nerve damage, eye damage, etc. Detection of the disease isn’t very easy and prediction is additionally costlier. In today’s situation, hospitals are extremely busy due to COVID-19 pandemic, and it might be revolutionary if one could know if they’re at risk of being diabetic without visiting a doctor. But the rise in Artificial Intelligence techniques can be used for disease prognosis. The objective of this study is to develop a model with significant accuracy to diagnose diabetes in patients. Moreover, this paper also presents an effective diabetes prediction model for better classification of diabetes and to enhance the accuracy in diabetes prediction using several machine learning algorithms. Different machine learning algorithms are utilized for early stage diabetes prediction, namely, Logistic Regression, Random Forest Classifier, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Gaussian Process Classifier, AdaBoost Classifier, and Gaussian Naïve Bayes. The performances of these models are measured on respective criteria like Accuracy, Precision, Recall, F-Measure, and Error. For this research work, latest available dataset dated 22nd July, 2020, is being utilized. Latest updated dataset will show comparatively better result.
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References
Kalyankar, G.D., Poojara, S.R., Dharwadkar, N.V.: Predictive analysis of diabetic patient data using machine learning and hadoop. In: International Conference On I-SMAC (2017). ISBN 978-1-5090-3243-3
Komi, M., Li, J., Zhai, Y., Zhang, X.: Application of data mining methods in diabetes prediction. In: Image, Vision and Computing (ICIVC), 2017 2nd International Conference on, pp. 1006–1010. IEEE (2017)
Perveen, S., Shahbaz, M., Guergachi, A., Keshavjee, K.: Performance analysis of data mining classification techniques to predict diabetes. Proced. Comput. Sci. 82, 115–121 (2016). https://doi.org/10.1016/j.procs.2016.04.016
Nai-Arun, N., Sittidech, P.: Ensemble learning model for diabetes classification. Adv. Mater. Res. 931–932, 1427–1431 (2014). https://doi.org/10.4028/www.scientific.net/AMR.931-932.1427
Orabi, K.M., Kamal, Y.M., Rabah, T.M.: Early predictive system for diabetes mellitus disease. In: Industrial Conference on Data Mining, pp. 420–427. Springer (2016)
Priyam, A., Gupta, R., Rathee, A., Srivastava, S.: Comparative analysis of decision tree classification algorithms. Int. J. Current Eng. Technol. 3, 334–337, 2277–4106 (2013). arXiv:ISSN
Esposito, F., Malerba, D., Semeraro, G., Kay, J.: A comparative analysis of methods for pruning decision trees. IEEE Trans. Pattern Anal. Mach. Intell. 19, 476–491 (1997). https://doi.org/10.1109/34.589207
Pradhan, M., Bamnote, G.R.: Design of classifier for detection of diabetes mellitus using genetic programming. Adv. Intell. Syst. Comput. 1, 7630770 (2014). https://doi.org/10.1007/978-3-319-11933-5
Sharief, A.A., Sheta, A.: Developing a mathematical model to detect diabetes using multigene genetic programming. Int. J. Adv. Res. Artif. Intell. (IJARAI) 3, 54–59 (2014). https://doi.org/10.14569/IJARAI.2014.031007
Mandal, S., Balas, V.E., Shaw, R.N., Ghosh, A.: Prediction analysis of idiopathic pulmonary fibrosis progression from OSIC dataset. In: 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, pp. 861–865 (2020). https://doi.org/10.1109/gucon48875.2020.9231239
Kumar, M., Shenbagaraman, V.M., Ghosh, A.: Predictive data analysis for energy management of a smart factory leading to sustainability Book Chapter, Springer. In: Favorskaya, M.N., Mekhilef, S., Pandey, R.K., Singh, N. (eds.) Innovations in Electrical and Electronic Engineering, pp. 765–773 (2020). ISBN 978-981-15-4691-4
Han, J., Rodriguez, J.C., Beheshti, M.: Discovering decision tree based diabetes prediction model. In: International Conference on Advanced Software Engineering and Its Applications, pp. 99–109. Springer (2008)
Mandal, S., Biswas, S., Balas, V.E., Shaw, R.N., Ghosh, A.: Motion prediction for autonomous vehicles from lyft dataset using deep learning. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, 2020, pp. 768–773. https://doi.org/10.1109/iccca49541.2020.9250790
UCI−Machine Learning Repository, Early stage diabetes risk prediction dataset. Data Set
Ho, T.K.: Random decision forests (PDF). In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995, pp. 278–282 (1995). Archived from the original (PDF) on 17 April 2016. Retrieved 5 June 2016
Ho, T.K.: The random subspace method for constructing decision forests (PDF). IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998). https://doi.org/10.1109/34.709601
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Palimkar, P., Shaw, R.N., Ghosh, A. (2022). Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-2164-2_19
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