Abstract
Human body turns the food consumed into energy, but when insulin doesn’t act in its way to convert the blood glucose into energy, then the glucose remains in the bloodstream and causes a life-threatening health issue called Diabetes Mellitus or Diabetes. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future will be suffering from diabetes. With the rapid development of machine learning, it has been applied to many aspects of medical health. So, for efficiently and effectively diagnosing the Diabetes Mellitus, a method is proposed using the ML Grid Search algorithm. In this method, Pima Indian Diabetic Dataset is used. This system has two phases: The training phase includes preprocessing, feature selection and instance evaluation is done and the test phase includes preprocessing, instance evaluation and disease prediction is done. For feature selection, the random forest feature selection is used and for classification, support vector regression, logistic regression and grid based random forest classifier is used. The proposed method of predicting the diabetes, the accuracy is almost 95.7% which is higher when compared to previous methods. Additionally, the proposed system provides an ability to the users to understand the resulting scenario over any language with the help of language processing in results. The Natural Language Processing concept is adapted over the proposed approach to identify the features of the resulting text and perform the language processing and display the exact language-oriented data to the users without any hurdle.
Similar content being viewed by others
References
Alberti, K. G., & Zimmet, P. Z. (1998). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabetic Medicine, 15(7), 539–553. https://doi.org/10.1002/(SICI)1096-9136(199807)15:7%3c539::AID-DIA668%3e3.0.CO;2-S
American Diabetes Association. (2012). Diagnosis and classification of diabetes mellitus. Diabetes Care, 35, S64–S71. https://doi.org/10.2337/dc12-s064
Chen, W., Chen, S., & Zhang, H. (2017). A hybrid prediction model for type 2 diabetes using machine learning and decision tree algorithms. National herbal basis of technology in China, IEEE, 5386-0497.
Cherifi, E. H., et al. (2021). Arabic grapheme-to-phoneme conversion based on joint multi-gram model. International Journal of Speech Technology, 24, 173–182.
Contreras, I., & Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: Literature review. Journal of Medical Internet Research, 20(5), e10775. https://doi.org/10.2196/10775
Dey, S. K., & Hossain, A. (2018). Implementation of a web application to predict diabetes disease: an approach using machine learning algorithm. In 2018 21st international conference of computer and information technology (ICCIT) (pp. 1–5). IEEE.
Faruque, Md. F., & Sarker, I. H. (2019, February). Performance analysis of machine learning techniques to predict diabetes mellitus. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1–4). IEEE. https://doi.org/10.1109/ECACE.2019.8679365.
Han, Wu., Yang, S., Huang, Z., He, J., & Wang, X. (2018). Type 2 diabetes mellitus prediction model based totally on information mining. Informatics in Medicine Unlocked, 10, 100–107.
Indoria, P., & Rathore, Y. K. (2018). A survey: detection and prediction of diabetes using machine learning techniques. International Journal of Engineering Research & Technology (IJERT), 7(3), 287–291.
Kaur, H., & Kumari, V. (2020). Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2018.12.004
Khalil, R. M., & Al-Jumaily, A. (2017). Machine learning based prediction of depression among type 2 diabetic patients. In 2017 12th international conference on intelligent systems and knowledge engineering, Science Direct, (pp. 1–5). IEEE.
Kim, J., Kim, J., & Kwak, M. J. (2017). Genetic prediction of type 2 diabetes using deep machneural network. Computational and Structural biotechnology, 109, 10–111.
Kumar, N. M. S., Eswari, T., Sampath, P., & Lavanya, S. (2015). Predictive methodology for diabetic data analysis in big data. Procedia Computer Science, 50, 203–208. https://doi.org/10.1016/j.procs.2015.04.069
Mercaldoa, F., Nardoneb, V., & Santoneb, A. (2017). Diabetes mellitus affected patients classification and diagnosis through machine learning techniques. Procedia Computer Science, Elesevier, 112, 2519–2528.
Mir, A., & Dhage, S. N. (2018). Diabetes disease prediction using machine learning on big data of healthcare. In 2018 fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1–6). IEEE.
Nguyen, B. P., Pham, H. N., & Tran, H. (2019). Predicting the onset of type 2 diabetes the use of huge and deep Neural Network with electronic health records. Computer Methods and Algotithms in Biomedicine, 182, 105055.
Perveen, S., & Shahbaz, M. (2019). Prognostic modelling and prevention of diabetes using machine learning technologies. Science Reports, 24, 41598.
Razavian, N., Blecker, S., Schmidt, A. M., Smith-McLallen, A., Nigam, S., & Sontag, D. (2015). Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data, 3(4), 277–287.
Robertson, G., Lehmann, E. D., Sandham, W., & Hamilton, D. (2011). Blood glucose prediction using artificial neural networks trained with the AIDA diabetes simulator: A proof-of-concept pilot study. Journal of Electrical and Computer Engineering, 2011, 1–11. https://doi.org/10.1155/2011/681786
Salian, S., & Harisekaran, D. G. (2015). Big data analytics predicting risk of readmissions of diabetic patients. International Journal of Science and Research, 4(4), 534–538.
Sneha, N., & Gangil, T. (2019). Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big data, 6(1), 1–19.
Sungeetha, D., Keerthana, G., & Vijayakumar, K. (2019). Design and implementation of finite state machine using quantum-dot cellular automata. International Journal of Reasoning-Based Intelligent Systems, 11(2), 186–193.
Thimmaraja, Y. G., Nagaraja, B. G., & Jayanna, H. S. (2021). Speech enhancement and encoding by combining SS-VAD and LPC. International Journal of Speech Technology, 24(1), 165–172.
Vijayakumar, K., & Arun, C. (2015). A survey on risk assessment in cloud migration. International Journal of Applied Engineering Research, 10, 66.
Vijayakumar, K., & Arun, C. (2018). Integrated cloud-based risk assessment model for continuous integration. International Journal of Reasoning-based Intelligent Systems, 10(3–4), 316–321.
Zia, U. A., & Khan, N. (2017). Predicting diabetes in medical datasets using machine learning techniques. International Journal of Scientific & Engineering Research, 8, 5.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Abokhzam, A.A., Gupta, N.K. & Bose, D.K. Efficient diabetes mellitus prediction with grid based random forest classifier in association with natural language processing. Int J Speech Technol 24, 601–614 (2021). https://doi.org/10.1007/s10772-021-09825-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-021-09825-z