A Machine Learning Model for Predicting of Chronic Kidney Disease Based Internet of Things and Cloud Computing in Smart Cities

Abdelaziz, Ahmed; Salama, Ahmed S.; Riad, A. M.; Mahmoud, Alia N.

doi:10.1007/978-3-030-01560-2_5

Cited by 42 publications

(19 citation statements)

References 24 publications

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“…The outcomes demonstrate that, this hybrid intelligent model is 97.8% accurate in predicting CKD. The proposed model is better than the majority of the models referred to in the associated research works by 64% [2]. Chronic Kidney Disease (CKD) affects the structure and functionality of kidney.…”

Section: Machine Learning Models and Neural Networkmentioning

confidence: 92%

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Anusha¹,

Vital²,

Sangeeta³

2019

IJRTE

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Chronic kidney disease (CKD) is one of the most widely spread diseases across the world. Mysteriously some of the areas in the world like Srilanka, Nicrgua and Uddanam (India), this disease affect more and it is cause of thousands of deaths particular areas. Now days, the prevention with utilizing statistical analysis and early detection of CKD with utilizing Machine Learning (ML) and Neural Networks (NNs) are the most important topics. In this research work, we collected the data form Uddanam (costal area of srikakulam district, A.P, India) about patient's clinical data, living styles (Habits and culture) and environmental conditions (water, land and etc.) data from 2016 to 2019. In this paper, we conduct the statistical analysis, Machine Learning (ML) and Neural Network application on clinical data set of Uddanam CKD for prevention and early detection of CKD. As per statistical analysis we can prevent the CKD in the Uddanam area. As per ML analysis Naive Bayes model is the best where the process model is constructed within 0.06 seconds and prediction accuracy is 99.9%. In the analysis of NNs, the 9 neurons hidden layer (HL) Artificial Neural Network (ANN) is very accurate than other all models where it performs 100% of accuracy for predicting CKD and it takes the 0.02 seconds process time.

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Section: Machine Learning Models and Neural Networkmentioning

confidence: 92%

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Anusha¹,

Vital²,

Sangeeta³

2019

IJRTE

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“…Therefore, cloud computing will enhance speed, sharpness, and cost savings by providing network access on demand for sharing computing resources, which can be scaled as required and rapidly provisioned. The combination of IoT and cloud computing plays a vital role in healthcare applications such as disease prediction intelligently in smart cities [100].…”

Section: Smart Data Center For Smart Citiesmentioning

confidence: 99%

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

et al. 2021

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The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

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“…A scalable cloud-based architecture was offered in [31] for teleophthalmology in Internet of Medical Things (IoMT) for age-related macular degeneration (AMD) prediction considering the security requirements. Also, a hybrid intelligent approach was proposed for chronic kidney disease prediction in cloud-based IoT environment, in [32]. Recently, a medical monitoring scheme for cloud-based IoT platforms was proposed in [7] which applied a variety of classification methods for predicting a combination of diabetes mellitus, renal disorder, hypertension, and heart disease.…”

Section: Remote Health Monitoring Framework and Architectures In Iotmentioning

confidence: 99%

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Akhbarifar

Javadi

Rahmani

et al. 2020

Pers Ubiquit Comput

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Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients’ health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients’ health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients’ health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients’ sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

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A Machine Learning Model for Predicting of Chronic Kidney Disease Based Internet of Things and Cloud Computing in Smart Cities

Cited by 42 publications

References 24 publications

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

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