Abstract
Now-a-days modern technology is used for health management and diagnostic strategy in the health sector. Machine learning usually helps in decision making for health issues using different models. Classification and prediction of disease are easily known with the help of machine learning techniques. The machine learning technique can be applied in various applications such as image segmentation, fraud detection, pattern recognition and disease prediction, etc. In the today’s world, maximum people are suffering from diabetes. The glucose factor in the blood is the main component of diabetes. Fluctuation of blood glucose level leads to diabetes. To predict the diabetes disease, machine learning and deep learning play major role which uses probability, statistics and neural network concepts, etc. Deep learning is the part of machine learning which uses different layers of neural network that decide classification and prediction of disease. In this chapter, we study and compare among different machine learning algorithms and deep neural networks for diabetes disease prediction, by measuring performance. The experiment results prove that convolution neural network based deep learning method provides the highest accuracy than other machine learning algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sneha, N., and T. Gangil. 2019. Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big Data 6 (1): 13.
Doupe, P., J. Faghmous, and S. Basu. Machine learning for health services researchers. Value in Health.
Kaur, H., and V. Kumari. 2018. Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics.
Das, H., B. Naik, and H.S. Behera. 2018. Classification of diabetes mellitus disease (DMD): a data mining (DM) approach. In Progress in computing, analytics and networking, 539–549. Singapore: Springer.
Sahani, R., C. Rout, J.C. Badajena, A.K. Jena, and H. Das. 2018. Classification of intrusion detection using data mining techniques. In Progress in computing, analytics and networking, 753–764. Singapore: Springer.
Das, H., A.K. Jena, J. Nayak, B. Naik, and H.S. Behera. 2015. A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification. In Computational intelligence in data mining, vol. 2, 461–471. New Delhi: Springer.
Pradhan, C., H. Das, B. Naik, and N. Dey. 2018. Handbook of research on information security in biomedical signal processing, 1–414. Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-5225-5152-2.
Pattnaik, P.K., S.S. Rautaray, H. Das, and J. Nayak (eds.). 2018. Progress in computing, analytics and networking. In Proceedings of ICCAN 2017, vol. 710. Springer.
Nayak, J., B. Naik, A.K. Jena, R.K. Barik, and H. Das. 2018. Nature inspired optimizations in cloud computing: applications and challenges. In Cloud computing for optimization: foundations, applications, and challenges, 1–26. Cham: Springer.
Mishra, B.B., S. Dehuri, B.K. Panigrahi, A.K. Nayak, B.S.P. Mishra, and H. Das. 2018. Computational intelligence in sensor networks, vol. 776. Studies in Computational Intelligence. Springer.
Kanchan, B.D., and M.M. Kishor. 2016. Study of machine learning algorithms for special disease prediction using principal of component analysis. In 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC), 5–10. IEEE.
Khalil, R.M., and A. Al-Jumaily. 2017. Machine learning based prediction of depression among type 2 diabetic patients. In 2017 12th international conference on intelligent systems and knowledge engineering (ISKE), 1–5. IEEE.
Dey, S.K., A. Hossain, and M.M. Rahman. 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). 1–5. IEEE.
Barakat, N., A.P. Bradley, and M.N.H. Barakat. 2010. Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Transactions on Information Technology in Biomedicine 14 (4): 1114–1120.
Sahoo, A.K., C. Pradhan, and B.S.P. Mishra. 2019. SVD based privacy preserving recommendation model using optimized hybrid item-based collaborative filtering. In 2019 international conference on communication and signal processing (ICCSP), 0294–0298. IEEE.
Sahoo, A.K., S. Mallik, C. Pradhan, B.S.P. Mishra, R.K. Barik, and H. Das. 2019. Intelligence-based health recommendation system using big data analytics. In Big data analytics for intelligent healthcare management, 227–246. Academic Press.
Jan, B., H. Farman, M. Khan, M. Imran, I.U. Islam, A. Ahmad, and G. Jeon. 2017. Deep learning in big data analytics: a comparative study. Computers & Electrical Engineering.
Zhao, R., R. Yan, Z. Chen, K. Mao, P. Wang, and R.X. Gao. 2019. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing 115: 213–237.
Miotto, R., F. Wang, S. Wang, X. Jiang, and J.T. Dudley. 2017. Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics 19 (6): 1236–1246.
Sahoo, A.K., C. Pradhan, R.K. Barik, and H. Dubey. 2019. DeepReco: deep learning based health recommender system using collaborative filtering. Computation 7 (2): 25.
Solanki, J.D., S.D. Basida, H.B. Mehta, S.J. Panjwani, and B.P. Gadhavi. 2017. Comparative study of cardiac autonomic status by heart rate variability between under-treatment normotensive and hypertensive known type 2 diabetics. Indian Heart Journal 69 (1): 52–56.
Baiju, B.V., and D.J. Aravindhar. 2019. Disease influence measure based diabetic prediction with medical data set using data mining. In 2019 1st international conference on innovations in information and communication technology (ICIICT), 1–6. IEEE.
Undre, P., H. Kaur, and P. Patil. 2015. Improvement in prediction rate and accuracy of diabetic diagnosis system using fuzzy logic hybrid combination. In 2015 international conference on pervasive computing (ICPC), 1–4. IEEE.
Prasad, S.T., S. Sangavi, A. Deepa, F. Sairabanu, and R. Ragasudha. 2017. Diabetic data analysis in big data with predictive method. In 2017 international conference on algorithms, methodology, models and applications in emerging technologies (ICAMMAET), 1–4. IEEE.
Hammoudeh, A., G. Al-Naymat, I. Ghannam, and N. Obied. 2018. Predicting hospital readmission among diabetics using deep learning. Procedia Computer Science 141: 484–489.
Aliberti, A., I. Pupillo, S. Terna, E. Macii, S. Di Cataldo, E. Patti, and A. Acquaviva. 2019. A multi-patient data driven approach to blood glucose prediction. IEEE Access.
Sisodia, D., and D.S. Sisodia. 2018. Prediction of diabetes using classification algorithms. Procedia Computer Science 132: 1578–1585.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sahoo, A.K., Pradhan, C., Das, H. (2020). Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making. In: Rout, M., Rout, J., Das, H. (eds) Nature Inspired Computing for Data Science. Studies in Computational Intelligence, vol 871. Springer, Cham. https://doi.org/10.1007/978-3-030-33820-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-33820-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33819-0
Online ISBN: 978-3-030-33820-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)