Architecture of the Platform for Big Data Preprocessing and Processing in Medical Sector

The paper presents the architecture of the platform for Big data preprocessing and processing. The method for prevention of the risk disease based on Probabilistic Production Dependencies is developed. The investigation is started from the platform for Big data in medical domain analysis. The platform consists of 6 layers: Data layer, Communication layers, Preprocessing layer, Data processing layer, User layer, Integrational layer. The platform architecture for Big data processing in medical sector is developed. The accuracy of the proposed method is estimated.

[1]  M. G. Ruano,et al.  Reliability of medical databases for the use of real word data and data mining techniques for cardiovascular diseases progression in diabetic patients , 2018, 2018 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE).

[2]  Oluwarotimi Williams Samuel,et al.  A joint resource-aware and medical data security framework for wearable healthcare systems , 2019, Future Gener. Comput. Syst..

[3]  Shan Xu,et al.  Cardiovascular risk prediction method based on test analysis and data mining ensemble system , 2016, 2016 IEEE International Conference on Big Data Analysis (ICBDA).

[4]  Ling Li,et al.  Towards Big Data Bayesian Network Learning - An Ensemble Learning Based Approach , 2014, 2014 IEEE International Congress on Big Data.

[5]  Olena Vovk,et al.  UNCERTAINTY REDUCTION IN BIG DATA CATALOGUE FOR INFORMATION PRODUCT QUALITY EVALUATION , 2018 .

[6]  A. Dolotov.  HYBRID SYSTEMS OF COMPUTATIONAL INTELLIGENCE EVOLVED FROM SELF- LEARNING SPIKING NEURAL NETWORK , 2010 .

[7]  D. F. Specht,et al.  Experience with adaptive probabilistic neural networks and adaptive general regression neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[8]  Volodymyr Melnykov,et al.  Using Big Data for Formalization the Patient’s Personalized Data , 2019, Procedia Computer Science.

[9]  Jignesh M. Patel,et al.  Big data and its technical challenges , 2014, CACM.

[10]  William Marsh,et al.  From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support , 2016, Artif. Intell. Medicine.

[11]  Natalia Melnykova.  Semantic Search Personalized Data as Special Method of Processing Medical Information , 2017 .

[12]  KhanmohammadiSina,et al.  An improved overlapping k-means clustering method for medical applications , 2017 .

[13]  Feng Zhou,et al.  A new prediction method for recommendation system based on sampling reconstruction of signal on graph , 2020, Expert Syst. Appl..

[14]  Malti Bansal,et al.  IoT & Big Data in Smart Healthcare (ECG Monitoring) , 2019, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon).

[15]  Akhtar Hussain Jalbani,et al.  Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network , 2017 .

[16]  Finn Verner Jensen,et al.  Bayesian Networks and Influence Diagrams , 1997 .

[17]  Jiajun Wu,et al.  Entity Abstraction in Visual Model-Based Reinforcement Learning , 2019, CoRL.

[18]  Esther-Lydia Silva-Ramírez,et al.  Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns , 2015, Appl. Soft Comput..

[19]  Tao Chen,et al.  Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning , 2019, IEEE Access.

[20]  Bianca Zadrozny,et al.  A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer's disease and mild cognitive impairment , 2014, Comput. Biol. Medicine.

[21]  Li Li,et al.  Practical Data Mid-Platform Design and Implementation for Medical Big Data , 2019, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[22]  Alistair A. Young,et al.  Big Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging , 2015, IEEE Journal of Biomedical and Health Informatics.

[23]  Ivan Izonin,et al.  An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble † , 2020, Sensors.

[24]  Solomia Fedushko,et al.  Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies , 2020, Electronics.

[25]  Vasyl Lytvyn,et al.  Methods of Building Intelligent Decision Support Systems Based on Adaptive Ontology , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[26]  Alejandro Maté,et al.  An Integrated Multidimensional Modeling Approach to Access Big Data in Business Intelligence Platforms , 2012, ER Workshops.

[27]  Oleksii K. Tyshchenko,et al.  A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning , 2019, Int. J. Appl. Math. Comput. Sci..

[28]  Yuriy Syerov,et al.  Development of Mobile System for Medical Recommendations , 2019, Procedia Computer Science.

[29]  Nilanjan Dey,et al.  Internet of Things and Big Data Technologies for Next Generation Healthcare , 2017 .