Nature Inspired Computing for Data Science pp 201-212 | Cite as
Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making
Chapter
First Online:
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.
Keywords
Convolutional neural network Deep learning Diabetes disease prediction Machine learning Performance evaluationReferences
- 1.Sneha, N., and T. Gangil. 2019. Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big Data 6 (1): 13.CrossRefGoogle Scholar
- 2.Doupe, P., J. Faghmous, and S. Basu. Machine learning for health services researchers. Value in Health.Google Scholar
- 3.Kaur, H., and V. Kumari. 2018. Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics.Google Scholar
- 4.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.Google Scholar
- 5.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.Google Scholar
- 6.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.Google Scholar
- 7.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.
- 8.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.Google Scholar
- 9.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.Google Scholar
- 10.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.Google Scholar
- 11.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.Google Scholar
- 12.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.Google Scholar
- 13.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.Google Scholar
- 14.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.CrossRefGoogle Scholar
- 15.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.Google Scholar
- 16.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.Google Scholar
- 17.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.Google Scholar
- 18.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.CrossRefGoogle Scholar
- 19.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.CrossRefGoogle Scholar
- 20.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.CrossRefGoogle Scholar
- 21.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.CrossRefGoogle Scholar
- 22.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.Google Scholar
- 23.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.Google Scholar
- 24.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.Google Scholar
- 25.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.CrossRefGoogle Scholar
- 26.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.Google Scholar
- 27.Sisodia, D., and D.S. Sisodia. 2018. Prediction of diabetes using classification algorithms. Procedia Computer Science 132: 1578–1585.CrossRefGoogle Scholar
Copyright information
© Springer Nature Switzerland AG 2020