Abstract
Diabetes mellitus is a problem that affects people around the world, nowadays the persons from young to old are suffering from diabetes. We can replace the traditional methods of diabetes prediction by modern technologies which saves time. There are many researches carried by researchers to predict diabetes, most of them have used pima Indian dataset. We are planning to use machine learning algorithms like Support Vector Machine and Naïve Bayes. By using these algorithms to predict diabetes we can save time and obtain more accurate results.
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References
Kandasamy JP, Balamurali S (2015) Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput Sci 47:45–51
Kaur H, Kumari V (2018) Predictive modelling and analytics for diabetes using a machine learning approach. Appl Comput Inform. https://doi.org/10.1016/j.aci.2018.12.004
Veena Vijayan V, Anjali C (2015) Prediction and diagnosis of diabetes mellitus - a machine learning approach. In: Recent advances in intelligent computational system (RAICS). IEEE
Sarwar MA, Kamal N, Hamid W, Shah MA (2018) Prediction of diabetes using machine learning algorithms in healthcare. In: Proceedings of the 24th international conference on automation & computing, Newcastle. IEEE
Dey SK, Hossain A, Rahman MM (2018) Implementation of web application to predict diabetes disease-an approach using machine learning algorithm. In: 21st international conference of computing and information technology (ICCIT). IEEE
Sudhir AM, Dhage N (2018) Diabetes disease prediction using machine learning on big data of healthcare. In: Fourth international conference on computing communication control and automation (ICCUBEA). IEEE
Joshi S, Borse M (2016) Detection and prediction of diabetes mellitus using back-propagation neural network. In: International Conference on Micro-Electronics and Telecommunication Engineering. IEEE
Barhate R, Kulkarni P (2018) Analysis of classifiers for prediction of type-2diabetes mellitus In: Fourth international conference on computing communication control and automation (ICCUBEA). IEEE
Saru S, Subashree S (2019) Analysis and prediction of diabetes using machine learning. Int J Emerg Technol Innov Eng 5(4):167–175. ISSN: 2394-6598
Anand A, Shakti D (2015) Prediction of diabetes based on personal lifestyle indicators. In: 2015 1st international conference on next generation technologies (NGCT-2015), 4–5 September 2015
Sonar P, Malini KJ (2019) Diabetes prediction using different machine learning approaches. In: Proceedings of the third international conference on computing methodologies and communication (ICCMC 2019). IEEE
Wei S, Zhao X, Miao C (2018) A comprehensive exploration to the machine learning techniques for diabetes identification. In: 4th world forum on Internet of Things. IEEE
Faruque MF, Iqbal, A, Sarkar H (2019) Performance analysis of machine learning techniques to predict diabetes mellitus. In: International conference on electrical, computer and communication engineering (ECCE). IEEE
Sowjanya K, Ayush Singhal DR, Choudahary C (2015) MobDBTest: a machine learning based system for predicting diabetes risk using mobile devices. In: International advanced computing conference (IACC). IEEE
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Amulya, K.J., Divya, S., Deepali, H.V., Divya, S., Ravikumar, V. (2021). A Survey on Diabetes Prediction Using Machine Learning. In: Kumar, A., Mozar, S. (eds) ICCCE 2020. Lecture Notes in Electrical Engineering, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-15-7961-5_97
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DOI: https://doi.org/10.1007/978-981-15-7961-5_97
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