Predictive analytics framework for accurate estimation of child mortality rates for Internet of Things enabled smart healthcare systems

Islam, Muhammad Usama; Usman, Muhammad; Mahmood, Azhar; Abbasi, Aaqif Afzaal; Song, Ohyoung

doi:10.1177/1550147720928897

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…Various studies also confirmed that these factors are crucial for under-five mortality [ 3 – 39 ]. From this study, we can confirm that the wealth index was one of the important factors for under-five mortality, which is in line with other studies [ 40 – 42 ].…”

Section: Discussionmentioning

confidence: 91%

Machine Learning Algorithms for understanding the determinants of under-five Mortality

et al. 2022

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Background Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality. Method The data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model’s accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen’s Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software. Result By applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen’s Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother’s education level, and birth order were identified as important factors influencing under-five mortality. Conclusion The neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.

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Section: Discussionmentioning

confidence: 91%

Machine Learning Algorithms for understanding the determinants of under-five Mortality

et al. 2022

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“…is research [25] highlights the Internet of things enabled smart devices framework for accurate estimation of child mortality.…”

Section: Related Workmentioning

confidence: 99%

Security Hardened and Privacy Preserved Android Malware Detection Using Fuzzy Hash of Reverse Engineered Source Code

Ali

Batool

Yousaf

et al. 2022

Security and Communication Networks

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The risk of malware has increased drastically in recent years due to advances in the IT industry but it also increased the need for malware analysis and prevention. Hackers inject malicious code using awful applications. In this research, a framework is proposed to identify malicious Android applications based on repacked malicious code. The sensitive features of android applications are extracted using source code. These extracted features are compared with existing malware signatures to identify repacked malicious android applications. Experiments are performed using 3490 android-based malware samples belonging to 21 different malware families. A threshold value for malware categorization is defined using fuzzy logic. If the fuzzy comparison match is greater than 40%, the application is malicious. Meanwhile, if the match is greater than 10% and less than 40%, the application is suspicious otherwise benign. Furthermore, the proposed framework presents around 74% of the repacked malware compared to other similar approaches.

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“…This technology includes machine learning (ML) and artificial intelligence (AI) integrated into the design and architecture of the entire system [31]. Some of the researches conducted in this area are blood pressure, heart-beat and glucose level [32][33][34], elderly healthcare [35], decentralized interoperable trust framework to enhances trustworthy factor (TF) estimation based on blockchain technology [36], estimation of under-five child mortality [37], and human activity recognition [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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Predictive analytics framework for accurate estimation of child mortality rates for Internet of Things enabled smart healthcare systems

Cited by 17 publications

References 19 publications

Machine Learning Algorithms for understanding the determinants of under-five Mortality

Machine Learning Algorithms for understanding the determinants of under-five Mortality

Security Hardened and Privacy Preserved Android Malware Detection Using Fuzzy Hash of Reverse Engineered Source Code

Analysis of factors affecting IoT-based smart hospital design

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