711
Views
6
CrossRef citations to date
0
Altmetric
Research Article

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model

&
Pages 1146-1168 | Received 06 Oct 2020, Accepted 23 Dec 2020, Published online: 11 Jan 2021

References

  • Aliberti A, Pupillo I, Terna S, Macii E, Di Cataldo S, Patti E, Acquaviva A. 2019. A multi-patient data-driven approach to blood glucose prediction. IEEE Access. 7:69311–69325.
  • Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J, Alrashed S, Olatunji SO. 2019. Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study. Comput Biol Med. 109:101–111.
  • Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. 2019. Artificial neural network methods for the solution of second order boundary value problems. CMC. 59(1):345–359.
  • Beno MM, Valarmathi IR, Swami SM, Rajakumar BR. 2014. Threshold prediction for segmenting tumour from brain MRI scans. Int J Imaging Syst Technol. 24(2):129–137.
  • Cai Z, Gu J, Chen H. 2017. A new hybrid intelligent framework for predicting Parkinson’s disease. IEEE Access. 5:17188–17200.
  • Chen M, Hao Y, Hwang K, Wang L, Wang L. 2017. Disease prediction by machine learning over big data from healthcare communities. IEEE Access. 5:8869–8879.
  • ChristalinLatha CB, Jeeva SC. 2019. Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform Med. 16: 1–9.
  • D M, R L, V S, V V, Sangaiah AK. 2019. Hybrid reasoning-based privacy-aware disease prediction support system. Comput Electr Eng. 73:114–127.
  • Dahiwade D, Patle G, Meshram E. 2019. Designing disease prediction model using machine learning approach. In 3rd International Conference on Computing Methodologies and Communication (ICCMC); p. 1211–1215.
  • Haq AU, Li JP, Memon MH, Khan J, Malik A, Ahmad T, Ali A, Nazir S, Ahad I, Shahid M. 2019. Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings. IEEE Access. 7:37718–37734.
  • Hashem S, Esmat G, Elakel W, Habashy S, Raouf SA, Elhefnawi M, Eladawy M, ElHefnawi M. 2018. Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients. IEEE/ACM Trans Comput Biol Bioinform. 15(3):861–868.
  • Hou Y, Luo CY, Yang J, Ou RW, Song W, Wei Q, Cao B, Zhao B, Wu Y, Shang H-F, et al. 2016. Prediction of individual clinical scores in patients with Parkinson's disease using resting-state functional magnetic resonance imaging. J Neurol Sci. 366:27–32.,
  • Jiang P, Wang X, Li Q, Jin L, Li S. 2019. Correlation-aware sparse and low-rank constrained multi-task learning for longitudinal analysis of Alzheimer's Disease. IEEE J Biomed Health Inform. 23(4):1450–1456.
  • Khan A, Uddin S, Srinivasan U. 2019. Chronic disease prediction using administrative data and graph theory: The case of type 2 diabetes. Expert Syst Appl. 136:230–241.
  • Lei B, Yang M, Yang P, Zhou F, Hou W, Zou W, Li X, Wang T, Xiao X, Wang S. 2020. Deep and joint learning of longitudinal data for Alzheimer's disease prediction. Pattern Recognit. 102:107247.
  • Li Q, Li MC, Lv L, Guo C, Lu K. 2017. A new prediction model of infectious diseases with vaccination strategies based on evolutionary game theory. Chaos, Solitons Fractals. 104:51–60. vol November
  • Li H, Li X, Ramanathan M, Zhang A. 2015. Prediction and informative risk factor selection of bone diseases. IEEE/ACM Trans Comput Biol Bioinform. 12(1):79–91.
  • Li F, Liu M. 2019. A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer's disease. J Neurosci Methods. 323:108–118.
  • Mirjalili S, Lewis A. 2016. The whale optimization algorithm. Adv Eng Softw. 95:51–67.
  • Mirjalili S, Mirjalili SM, Hatamlou A. 2016. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Applic. 27(2):495–513.
  • Mohan S, Thirumalai C, Srivastava G. 2019. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. 7:81542–81554.
  • Nilashi M, Ibrahim O. b, Ahmadi H, Shahmoradi L. 2017. An analytical method for diseases prediction using machine learning techniques computers & chemical engineering. Comput Chem Eng. 1062:212–223.
  • Nirmala Sreedharan NP, Ganesan B, Raveendran R, Sarala P, Dennis B, Boothalingam R R. 2018. Grey Wolf optimisation-based feature selection and classification for facial emotion recognition. IET Biometrics. 7(5):490–499. 9
  • Rao VSH, Kumar MN. 2013. Novel Approaches for predicting risk factors of atherosclerosis. IEEE J Biomed Health Inform. 17(1):183–189.
  • Rao RV. 2016. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput. 7:19–34.
  • Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh VM, Guo H, Hamdia K, Zhuang X, Rabczuk T. 2020. An energy approach to the solution of partial differential equations in computational mechanics via machine learning:Concepts, implementation and applications. Comput Methods Appl Mech Eng. 362:112790.
  • Sengupta S, Das AK. 2017. Particle swarm optimization based incremental classifier design for rice disease prediction. Comput Electron Agric. 140:443–451. volAugust
  • Ullah R, Khan S, Ali H, Chaudhary II, Bilal M, Ahmad I. 2019. A comparative study of machine learning classifiers for risk prediction of asthma disease. Photodiagn Photodyn Ther. 28:292–296.
  • Usama M, Ahmad B, Xiao W, Hossain MS, Muhammad G. 2019. Self-attention based recurrent convolutional neural network for disease prediction using healthcare data. Comput Methods Programs Biomed. 190:1–32. Available online 11.
  • Vinarti RA. 2019. Knowledge representation for infectious disease risk prediction system: a literature review. Procedia Comput Sci. 161:821–825.
  • Wei H, Shan C, Hu C, Zhang And Yu YX. 2019. Software defect prediction via deep belief network. Chin J Electron. 28(5):925–932.
  • Weng C-H, Huang TC-K, Han R-P. 2016. Disease prediction with different types of neural network classifiers. Telematics Inform. 33(2):277–292.
  • Xing W, Bei Y. 2020. Medical health big data classification based on KNN classification algorithm. IEEE Access. 8:28808–28819.
  • Xue Q, Chuah MC. 2018. New attacks on RNN based healthcare learning system and their detections. Smart Health. 9-10:144–157.
  • Yu L, Su R, Wang B, Zhang L, Zou Y, Zhang J, Gao L. 2017. Prediction of novel drugs for hepatocellular carcinoma based on multi-source random walk. IEEE/ACM Trans Comput Biol Bioinform. 14(4):966–977.
  • Zhang C, Zhu L, Xu C, Lu R. 2018. PPDP: An efficient and privacy-preserving disease prediction scheme in cloud-based e-Healthcare system. Future Gener Comput Syst. 79(1):16–25.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.