Multi-objective Chaotic Butterfly Optimization with Deep Neural Network based Sustainable Healthcare Management Systems

Sustainable healthcare systems are developed to priorities healthcare services involving difficult decision-making processes. Besides, wearables, internet of things (IoT), and cloud computing (CC) concepts are involved in the design of sustainable healthcare systems. In this study, a new Multi-objective Chaotic Butterfly Optimization with Deep Neural Network (MOCBOA-DNN) is presented for sustainable healthcare management systems. The goal of the MOCBOA-DNN technique aims to cluster the healthcare IoT devices and diagnose the disease using the collected healthcare data. The MOCBOA technique is derived to perform clustering process and also to tune the hyperparameters of the DNN model. Primarily, the clustering of IoT healthcare devices takes place using a fitness function to select an optimal set of cluster heads (CHs) and organize clusters. Followed by, the collected healthcare data are sent to the cloud server for further processing. Furthermore, the DNN model is used to investigate the healthcare data and thereby determine the presence of disease or not. In order to ensure the betterment of the MOCBOA-DNN technique, an extensive simulation analysis take place. The experimental results portrayed the supremacy of the MOCBOA-DNN technique over the other existing techniques interms of diverse evaluation parameters.

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A deep neural network with a fuzzy multi-objective optimization model for fault analysis in an elastic optical network

Optical Switching and Networking ◽

10.1016/j.osn.2021.100644 ◽

2022 ◽

Vol 43 ◽

pp. 100644

Author(s):

André Luiz Ferraz Lourenço ◽

Amílcar Careli César

Keyword(s):

Neural Network ◽

Optimization Model ◽

Deep Neural Network ◽

Optical Network ◽

Fault Analysis ◽

Multi Objective Optimization ◽

Multi Objective ◽

Elastic Optical Network

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Weighted Deep Neural Network Based Clinical Decision Support System for the Determination of Fetal Health

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4378.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 8564-8569

Keyword(s):

Neural Network ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Deep Neural Network ◽

Predictive Analytics ◽

Clinical Decision ◽

Svm Classifier ◽

Healthcare Data ◽

The Individual

Healthcare industry is undergoing changes at a tremendous rate due to healthcare innovations. Predictive analytics is increasingly being used to diagnose the patient’s ailments and provide actionable insights into already existing healthcare data. The paper looks at a decision support system for determining the health status of the foetus from cardiotographic data using deep learning neural networks. The foetal health records are classified as normal, suspect and pathological. As the multiclass cardiotographic datset of the foetus shows a high degree of imbalance a weighted deep neural network is applied. To overcome the accuracy paradox due to the multiclass imbalance, relevant metrics such as the sensitivity, specificity, F1 Score and Gmean are used to measure the performance of the classifier rather than accuracy. The metrics are applied to the individual classes to ensure that the positive cases are identified correctly. The weighted DNN based classifier is able to classify the positive instances with Gmean score of 91% which is better than than the SVM classifier.

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A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment

Sensors ◽

10.3390/s19153334 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3334 ◽

Cited By ~ 5

Author(s):

Fei Li ◽

Min Liu ◽

Gaowei Xu

Keyword(s):

Simulation Analysis ◽

Fitness Function ◽

Routing Algorithm ◽

Optimal Path ◽

Ant Colony ◽

Local Optimum ◽

Manufacturing Environment ◽

Multi Objective ◽

Search Path ◽

Manufacturing Environments

In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR’s improvement in performance.

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India’s rural healthcare systems: structural modeling

International Journal of Health Care Quality Assurance ◽

10.1108/ijhcqa-02-2017-0020 ◽

2018 ◽

Vol 31 (7) ◽

pp. 757-774 ◽

Cited By ~ 2

Author(s):

Dinesh Kumar

Keyword(s):

Structural Model ◽

Healthcare Services ◽

Healthcare Systems ◽

Climatic Conditions ◽

Healthcare Management ◽

Driving Factors ◽

Rural Healthcare ◽

Content Type ◽

Rural Health Center ◽

Interpretive Structural Model

Purpose The purpose of this paper is to identify factors related to rural healthcare services and establish a hierarchical model for the effective rural healthcare management in India. Design/methodology/approach A questionnaire survey identified and correlated numerous factors related to the Uttarakhand rural healthcare systems. Experts opinion were translated into a reachability matrix and an interpretive structural model. A fuzzy matriced impacts croises-multiplication applique and classment (FMICMAC) analysis arranged the factors as hierarchical stages using their driving power. Findings The interpretive structural and FMICMAC hierarchical models suggest four key driving factors: diseases, climatic conditions, population growth and political pressure. Practical implications Despite numerous issues, rural healthcare services can be improved by considering key driving factors that could be used as a prediction tool for policy makers. Originality/value Results demonstrate that population control, coordinating services with local bodies and rural health center annual maintenance can be game changers toward better healthcare services.

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A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

Inventions ◽

10.3390/inventions6030045 ◽

2021 ◽

Vol 6 (3) ◽

pp. 45

Author(s):

Prathamesh Churi ◽

Ambika Pawar ◽

Antonio-José Moreno-Guerrero

Keyword(s):

Healthcare System ◽

Large Population ◽

Healthcare Systems ◽

Healthcare Management ◽

Healthcare Sector ◽

Sensitive Data ◽

Data Breaches ◽

Healthcare Data ◽

Privacy Issues

Background: According to the renowned and Oscar award-winning American actor and film director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences caused by privacy and data breaches. The Indian healthcare industry is one of the largest and rapidly developing industry. Overall, healthcare management is changing from disease-centric into patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management, and the privacy of patient records must receive equal attention. Purpose: This paper mainly presents the utility and privacy factors of the Indian healthcare data and discusses the utility aspect and privacy problems concerning Indian healthcare systems. It defines policies that reform Indian healthcare systems. The case study of the NITI Aayog report is presented to explain how reformation occurs in Indian healthcare systems. Findings: It is found that there have been numerous research studies conducted on Indian healthcare data across all dimensions; however, privacy problems in healthcare, specifically in India, are caused by prevalent complacency, culture, politics, budget limitations, large population, and existing infrastructures. This paper reviews the Indian healthcare system and the applications that drive it. Additionally, the paper also maps that how privacy issues are happening in every healthcare sector in India. Originality/Value: To understand these factors and gain insights, understanding Indian healthcare systems first is crucial. To the best of our knowledge, we found no recent papers that thoroughly reviewed the Indian healthcare system and its privacy issues. The paper is original in terms of its overview of the healthcare system and privacy issues. Social Implications: Privacy has been the most ignored part of the Indian healthcare system. With India being a country with a population of 130 billion, much healthcare data are generated every day. The chances of data breaches and other privacy violations on such sensitive data cannot be avoided as they cause severe concerns for individuals. This paper segregates the healthcare system’s advances and lists the privacy that needs to be addressed first.

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A Forecasting Model for Patient Arrivals of an Emergency Department in Healthcare Management Systems

Advances in Healthcare Information Systems and Administration - Intelligent Systems for Healthcare Management and Delivery ◽

10.4018/978-1-5225-7071-4.ch012 ◽

2019 ◽

pp. 266-284 ◽

Cited By ~ 1

Author(s):

Melih Yucesan ◽

Muhammet Gul ◽

Suleyman Mete ◽

Erkan Celik

Keyword(s):

Neural Network ◽

Human Resources ◽

Medical Devices ◽

Moving Average ◽

Healthcare Management ◽

Management Systems ◽

Forecasting Model ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Artificial Neural Network Ann

Emergency departments (EDs) are one of the most valuable departments of healthcare management systems. Patient arrivals at the EDs are crucial for planning of the future. Accurate forecasting of patient arrivals contributes to better organized human resources and medical devices in the EDs. Therefore, in this chapter, the authors aim to develop a hybrid model including the methods of autoregressive integrated moving average with external variables (ARIMAX) and artificial neural network (ANN) in a hospital ED. The arrival data was collected from the hospital information system of a public hospital in eastern Turkey. The model incorporates factors related to ED arrivals such as climatic and calendar variables. By the aid of the proposed model, an insight to arrangement and planning of ED resources can be provided in a better way.

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Insights for implementing change in healthcare

British Journal of Healthcare Management ◽

10.12968/bjhc.2018.0022 ◽

2020 ◽

Vol 26 (1) ◽

pp. 20-26

Author(s):

Joanna DaCosta

Keyword(s):

Organisational Change ◽

Healthcare Services ◽

Healthcare Systems ◽

Organisational Culture ◽

Healthcare Management ◽

Planned Change ◽

Workplace Culture ◽

Successful Implementation ◽

Management Skills ◽

Implementing Change

Healthcare systems are complex; this is reflected in their diverse structures, cultures and services. Organisational culture is an important aspect to consider when planning any intervention changes. Working in and with teams that include members from different professional and organisational backgrounds, and successfully managing these potential tensions, can be challenging. This article reviews the elements required to instigate organisational change when planning an intervention and the possible barriers preventing doctors from full engagement in these projects. It discusses the model of ‘planned change’, which was first described by Lewin in 1947, and its evolution through recent decades as a framework for organisational change. It also emphasises the importance of workplace culture in the successful implementation of change within healthcare services and argues that a stronger emphasis on management skills is needed in doctor training. Doctors are an underused resource in healthcare management and should be encouraged to take a more proactive role.

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