5G-Smart Diabetes Towards Personalized Diabetes Diagnosis with Healthcare Big Data Clouds

Abstract

Ongoing advances in remote systems administration and large information innovations, for example, 5G organizations, clinical enormous information examination, and the Web of Things, alongside late improvements in wearable registering and man-made consciousness, are empowering the turn of events and execution of creative diabetes observing frameworks and applications. Because of the long lasting and methodical mischief endured by diabetes patients, planning successful techniques for the conclusion and treatment of diabetes is basic. In view of our exhaustive examination, this article orders those techniques into Diabetes 1.0 and Diabetes 2.0, which display lacks as far as systems administration and knowledge. Subsequently, we want to plan a practical, savvy, and clever diabetes determination arrangement with customized treatment. In this article, we initially propose the 5G-Savvy Diabetes framework, which consolidates the cutting edge advancements, for example, wearable 2.0, AI, and enormous information to produce far reaching detecting and examination for patients experiencing diabetes. Then, at that point, we present the information sharing instrument and customized information examination model for 5G-Savvy Diabetes. Last but not least, we develop a 5G-Smart Diabetes testbed that incorporates big data clouds, smart clothing, and smartphones. Our system is capable of effectively providing patients with personalized diagnosis and treatment recommendations, as demonstrated by the experimental results.

Introduction

I. INTRODUCTION

Diabetes is a very normal persistent sickness from which almost 8.5 percent of the world populace endure; 422 million individuals around the world need to battle with diabetes. It is pivotal to note that type 2 diabetes mellitus makes up around 90 percent of the cases [1]. All the more fundamentally, the circumstance will be more awful, as revealed in [2], with more young people and youth becoming powerless to diabetes too.

Because of the way that diabetes has an enormous effect on worldwide prosperity and economy, further developing strategies for the prevention is dire also, treatment of diabetes Moreover, different elements can cause the sickness, like ill-advised and unfortunate way of life, weak feeling status, alongside the gathered pressure from society and work. However, the 5G-Smart Diabetes system, which incorporates cutting-edge technologies such as machine learning, medical big data, social networking, smart clothing, and so on, is our first proposal for a diabetes solution of the next generation [10]. Then, at that point, we present the information sharing system and customized information investigation model for 5G-Shrewd Diabetes. At long last, based on the savvy clothing, cell phone, and enormous information medical services mists, we construct a 5G-Shrewd Diabetes testbed and give the investigation results. Besides, the"5G" in 5G-Shrewd Diabetes has a two-crease meaning. On one hand, it alludes to the 5G innovation that will be embraced as the correspondence framework to understand top notch and consistent observing of the physiological conditions of patients with diabetes and to give treatment administrations to such patients without limiting their opportunity. On the other hand, "5G" alludes to the accompanying "5 objectives": cost-effectiveness, ease of use, individualization, sustainability, and intelligence

II. LITERATURE SURVEY

1. The paper titled “An Integrated Approach of Diet and Exercise Recommendations for Diabetes Patients” stated that:

They have gone through an ontology-based integrated approach to unite knowledge from various areas to urge diet and practice suggestions for diabetes. The arrangement is created as a Semantic Healthcare Assistant for Diet and Exercise (SHADE). For every domain (person, diabetes, food, and exercise) they have represented separate ontology alongside rules then an integrated ontology connects these individual ontologies.

2. The paper named "An Interactive Healthcare System with Personalized Diet and Exercise Guideline Recommendation" expressed that:

This system analyzes the results of the traditional physical survey to live health risk and supply personalized health maintenance services for users in terms of diet and exercise guideline recommendations. They got some interactive ways for users to simply feedback their vital signs into the organization and quickly get suggestions for health management from the arrangement. Foremost, they practice the physical examination result because the data to be examined. It’s real convenient for users at a very low price. Second, the scheme design is extendable, so this is usually simply adjusted to compute for any chronic ailments, yet other kinds of diseases.

3. The report titled “PhytoCloud: A Gamified Mobile Web Application to Modulate Diet and Physical Activity of Women with Breast Cancer” stated that:

The story presents a user-centered approach of developing a Mobile Web App that focuses on breast cancer patients following their specific dietary, physical, and mental requirements counting on the phase of their medical treatment. The planning of PhytoCloud is being identified, a gamified Mobile Web App that lets users to record their dietary habits and physical action and motivate their consumption of food with estrogen-like properties (phytoestrogens) which are related to the prevention of reappearance of breast malignant neoplastic disease

4. The report entitled “Healthcare for patients with interstitial cystitis/bladder pain syndrome based on internet health education” stated that:

The objective of this study is to utilize Internet interruption for caring for Interstitial Cystitis patients to ease their pain and annoying symptoms. Healthcare education was conducted through the network by asking the patients, separated into the discipline and control groups to work out contraindications, drug abuses, and behaviors weekly to remind and consolidate important rules for promoting quality of life. The E-health scheme was shown to be efficacious in improving the QOL of IC patients through the interposition of the Internet healthcare education for the consolidation of healthy dieting habits and lifestyle.

III. METHODOLOGY

Technique and Hybrid classifier algorithm is used to detect diabetes mellitus. Overall architecture of the system This study aims to propose a new model for diabetics classification. Numerous algorithms and different approaches have been applied, such as traditional machine learning algorithms, ensemble learning approaches and association rule learning in order to achieve the best classification accuracy. The methods employed in this research are split by the four main phases of the research work, which are the problem formulation phase, the dataset collection phase, and the experimentation phase and the results summarizing. This research started with formulating the research problem that is reviewing of the literature and formulating of the research problem. After the research problem formulation, this research identified the scope of the research, the objectives, and limitations of the research procedure. The second phase of the study is the dataset collection. The dataset items were collected from National Institute of Diabetes and Digestive and Kidney Diseases. The third phase of the study was the data preparation which included: - Converting Data to Appropriate format - Data Preprocessing - Use Machine Learning to manipulate Data

IV. SYSTEM ANALYSIS

A. Existing System

They have gone through a philosophy based incorporated way to deal with join information from different regions to encourage diet and practice ideas for diabetes. The plan is made as a Semantic Medical care Partner for Diet and Exercise (SHADE). For each area (individual, diabetes, food, and exercise) they have addressed separate philosophy close by rules then a coordinated philosophy interfaces these singular ontologies.

B. Proposed System

The web application tells us all about common diseases, feminine diseases, recipes as home remedies, and some practice sessions and yoga asanas. It additionally comprises of an intuitive medical services bot that gives a customized diet plan and furthermore analyze infections from the manifestations given by users.

V. ARCHITECTURE

Compared to the intrinsic hospital-oriented features of Diabetes 1.0 and Diabetes 2.0, 5G-Smart Diabetes realizes effective prevention and post-hospitalization treatment of diabetes. Physiological monitoring is no longer limited to blood glucose detection but includes other critical physiological indicators. Effective measures are taken to monitor the real life and exercise of a user. Comprehensive conditions of the user are monitored in a long-term and sustainable fashion. The system architecture of 5G-Smart Diabetes is shown in Fig. 1, which includes three layers: the sensing layer, personalized diagnosis layer, and data sharing layer.

VII. ACKNOWLEDGEMENT

We are appreciative and might want to exact our true appreciation towards Prof. Mona Deshmukh for her continued guidance, support, and encouragement in creating this task possible. Our sincere thanks to all the lecturers who helped us in our effort, gave valuable advice, and thus made our journey possible. Last but not least we would also wish to thank our family and acquaintances for their constant support, motivation, and belief in us.

Conclusion

In this article, we first propose a 5G-Smart Dia-betes system that includes a sensing layer, a personalized diagnosis layer, and a data sharing layer. Compared to Diabetes 1.0 and Diabetes 2.0, this system can achieve sustainable, cost-ef-fective, and intelligence diabetes diagnosis. Then we propose a highly cost-efficient data sharing mechanism in social space and data space. In addition, using machine learning methods, we present a personalized data analysis model for 5G-Smart Diabetes. Finally, based on the smart clothing, smartphone and data center, we build a 5G-Smart Diabetes testbed. The experimental results show that our system can provide per-sonalized diagnosis and treatment suggestions to patients

References

[1] S. Mendis,“Global Status Report on Noncommunicable Diseases 2014,” WHO, tech. rep.; [Online] Available: http:// www.who.int/ nmh/publications/ncd-status-report-2014/en/, accessed Jan. 2015. [2] F. Florencia et al., IDF Diabetes Atlas, 6th ed., Int’l. Diabetes Federation, tech. rep.; [Online] Available: http://www. diabetesatlas.org/, accessed Jan. 2016. [3] M. Chen et al.,“Disease Prediction by Machine Learning over Big Healthcare Data,” IEEE Access, Vol. 5, pp. 8869- 79, 2017. [4] O. Geman, I. Chiuchisan, R. Toderean,“Application of Adaptive Neuro-Fuzzy Inference System for Diabetes Classification and prediction},” Proc. 6th IEEE Int’l. Conf. E-Health and Bioengineering, Sinaia, Romania, pp. 639-642, 2017. [5] S. Fong, et al.,“Real-Time Decision Rules for Diabetes Therapy Management by Data Stream Mining,” IT Professional, Vol. 26, No. 99, pp. 1-8, 2017. [6] B. Lee, J. Kim,“Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides Based on Machine Learning,” IEEE J. Biomed. Health Info., Vol. 20, No. 1, pp. 39-46, 2016. [7] M. Hossain, et al.,“Big Data-Driven Service Composition Using Parallel Clustered Particle Swarm Optimization in Mobile Environment,” IEEE Trans. Serv. Comp., Vol. 9, No. 5, pp. 806--17, 2016. [8] M. Hossain,“Cloud-Supported Cyber-Physical Localization Framework for Patients Monitoring,” IEEE Sys. J., Vol. 11, No. 1, pp. 118-27, 2017. [9] P. Pesl, et al.,“An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results,” IEEE J. Biomed. Health Info., Vol. 20, No. 1, pp. 11-17, 2016. [10] M. Chen et al.,“Wearable 2.0: Enable Human-Cloud Integration in Next Generation Healthcare System,” IEEE Commun. Mag., Vol. 55, No. 1, pp. 54-61, 2017. [11] E. Marie et al.,“Diabetes 2.0: Next-Generation Approach to Diagnosis and Treatment,” Brigham Health Hub, tech. rep.; [Online] Available: https://brighamhealthhub.org/diabetes2-0-next-generation-ap-proach-to-diagnosis-and-treatment, 2017, accessed Feb. 2017

Copyright

Copyright © 2023 Veeresh Biradar, Dr. T Senthil Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.