Disease Prediction in Health Care Communities: 1011326

The rhetorical analysis is based on the research paper analysing the technology of disease prediction with the help of machine learning on the basis of big data that is to be implemented on various healthcare communities. Along the growth of big data in the field of healthcare organisations it is now possible to analyse various diseases, taking care of the patients is getting more convenient. In the paper it is discussed on the basis of the research paper that describes the algorithm of machine learning. Though it is challenging to give decisions based on incomplete data, for this reason reconstruction of the whole amount of data is very necessary. In the paper it is discussed about algorithm for risk prediction which is based on CNN. Here is discussed on the different algorithms of CNN those are CNN-UDRP and CNN-MDRP. After that the results are provided accordingly.

The people of America is highly effected by the chronic disease that is caused due to the increasing life standards of the people (Polzin and Larry). Even there are different countries that are getting affected by these type of disease (Zwar et al.). Due to this reasons it is important to implement certain steps to stop this type of problems. With the use of EHR it is possible to collect huge amount of data or big data that can be used to analyse the health status of a certain health care organisation. It is also done with the use of PHR. With these certain analysis it is possible to detect the high risks patients as of more priority is needed for them. Like this there are certain implementations that can be done to help the health care systems. With the use of CNN the unstructured data are extracted that is used for analysis. Various other solutions are provided to solve and improve the use of big data in the healthcare organisations.

The authors who have worked on the research analysis are Min Chen, Yixue Hao, Kai Hwang, Lu Wang and Lin Wang (Chen et al.). All of the authors who have worked on the research are highly educated and they deserve the credibility to work on the specific topic based on their specialisation. All the authors who have worked on the topic are absolutely apparent biased and they do not have any personal interest regarding the outcome of the research result. All of the authors are represented by a specific organisation such as, Min Chen is an assistant professor of the School of Computer Science and engineering that is under the Seoul National University and Min Chen is sponsored by this specific organisation. Yixue Hao is sponsored by the School of Computer Science and Technology where from is pursuing his Ph.D degree. Kai Hwang got sponsored from Electrical Engineering and Computer Science under the university of Southern California where is a professor by his profession. Lu Wang got his sponsorship from the School of Computer Science and Technology where from he pursued his masters. Next comes Lin Wanf, she was sponsored by the Brown University where from she have pursued her Ph.D. degree.

In the thesis the authors have tried to propose a multimodal risk prediction that predicts risks that uses neural network (Zeestraten et al.). The algorithm uses structured data and also unstructured data present in the hospital. According to the thesis the author says that the data analytics is not only dependent on data types and the CNN-UDRP is the algorithm, which predicts disease faster (Jadhav et al.). In the following research the authors have analysed the problems that are faced in the health care organisations and thus utilises big data of the organisation to support the patients better. Thus to support the patients and to support other risks of the health care organisation the authors have chosen to study on the issue. The purpose of the author is to inform that there is a big risk in the healthcare organisations to take care of the patients and other issues related to health care.  The authors have studied the problems and have implemented the algorithm (CNN) that mines the structured and unstructured data and gives information based on analysis of the data. After the study authors have further criticised that in case of any diverse data it would hamper in mining the data (Cheng, Chih-Wen and May). Besides in case of any complex diseases only depending upon the data and analysing in not a convenient way, in that case the accuracy level becomes low (Andreu-Perez et al.). As the author discussed in the report the study is based on the medical care organisations and is not to any particular organisation rather it is specified for every medical organisations. Thus the author have intended this study to the people who are neutral from their side. The motive of the author is to better the medical field strategy. In the research the authors have applied a combination of appeals as because, there is logic behind the study, besides of which emotions acts into account because regarding the patients who usually suffers in the organisations. This there is the combination of ethos and logos in the study but there is no pathos in the study because every details mentioned in the study is based on research and analysis (Ting and Su Hie). In the research the writer have analysed a situation based on which he/she have imposed the research, so the author have done inductive reason n and thus have structured the argument. Though the research is observed thoroughly there is no kind of fallacies found in the complete research. In the particular research paper the authors have entirely used formal way for diction of all the words, arrangements and the accuracy is also formal. In the research paper the authors have used statistics and dialogue, but no quotation was applied in the paper. The reason behind using of statistical data is to specify the research work and findings clearly and specifically. Even there are certain collection of data that are mentioned in the paper to specify certain findings. All of the people have highly praised the work and mentioned it to be a great work (Asif et al.). In the rhetorical analysis performed on the paper there is no such viewpoints found that will oppose the research work (Saleh et al.).

The rhetorical report concludes the analyse of the research  paper on disease prediction in healthcare over big data imposing machine learning written by Chen, Hao, Hwang, Wang and Wang. By implementing this process and by analysing the structured data and unstructured data it will be possible to manage the healthcare organisations easily and securely.

Reference-

 
   
Ting, Su Hie. “Ethos, Logos and Pathos in University Studentsā€™ Informal Requests.” GEMA OnlineĀ® Journal of Language Studies 18.1 (2018).   Saleh, Heba, et al. “IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment.” IEEE Access 7 (2019): 5412-5420.  

Andreu-Perez, Javier, et al. “Big data for health.” IEEE journal of biomedical and health informatics 19.4 (2015): 1193-1208.

Asif, Muhammad, et al. “Annotation of Software Requirements Specification (SRS), Extractions of Nonfunctional Requirements, and Measurement of Their Tradeoff.” IEEE Access 7 (2019): 36164-36176.

Chen, Min, et al. “Disease prediction by machine learning over big data from healthcare communities.” Ieee Access 5 (2017): 8869-8879.

Cheng, Chih-Wen, and May D. Wang. “Healthcare Data Mining, Association Rule Mining, and Applications.” Health Informatics Data Analysis. Springer, Cham, 2017. 201-210.

Jadhav, Saiesh, et al. “Disease Prediction by Machine Learning from Healthcare Communities.” (2019).

Polzin, David J., and Larry D. Cowgill. Chronic Kidney Disease, An Issue of Veterinary Clinics of North America: Small Animal Practice, E-Book. Vol. 46. No. 6. Elsevier Health Sciences, 2016.

Zeestraten, Eva A., et al. “Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease.” Neurology 89.18 (2017): 1869-1876.

Zwar, Nicholas, et al. “A systematic review of chronic disease management.” (2017).