Air quality monitoring for Smart eHealth system using firefly optimization and support vector machine

Rahi, Pankaj; Sood, Swati; Bajaj, Rohit; Kumar, Yogesh

doi:10.1007/s41870-021-00778-9

Cited by 34 publications

(3 citation statements)

References 18 publications

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“…Rahi et al 2021 [ 21 ] introduced a firefly-optimized support vector machine (FSVM) for monitoring air quality in smart eHealth systems. This work aimed to predict the air quality for reducing airborne allergies and treatment cost burden.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] The Influence of Air Pollution on Pulmonary Disease Incidence Analyzed Based on Grey Correlation Analysis

Jiao

Gong

Wang

et al. 2022

Contrast Media & Molecular Imaging

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Air pollution is a primary health threat issue worldwide because it is closely concerned with respiratory diseases. A random survey reported that around 7 million people died because of ambient and household air pollution. Especially, the people suffering from asthma and chronic obstructive pulmonary disease (COPD) are highly affected by air pollutants. The air pollution components induce asthma onset and COPD acute exacerbation, which leads to maximized mortality and morbidity rate. Therefore, the influence of air pollution on COPD should be examined continuously to minimize the mortality rate. Several methods are presented in this field to investigate the relationship between health and pollutants. However, the existing approaches are only predicting the short-term data and have difficulties such as computation time, redundant data in large data analysis, and data continuity. Then, this research introduced the meta-heuristic optimized grey correlation analysis (MH-GCA) to solve the research difficulties. The correlation analysis has several models that identify the relationship between the pollution factors with COPD disease. The method analysis of the particulate matter (〖PM〗_10) in air pollution is more relevant to COPD and lung cancer disease. The grey analysis uses the uncertainty concept to identify the particle influence on air pollution. In the analysis, the cuttlefish optimization algorithm was applied to select more relevant features from the pollutant list that reduces the computation time and correlation analysis rate. The introduced system was evaluated using the air quality dataset and COPD dataset developed with the help of the MATLAB tool. The system increases the influence recognition accuracy (2.48%) and MCC (3.11%) and decreases the error rate (55.89%) for different pollutants.

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Section: Related Workmentioning

confidence: 99%

[Retracted] The Influence of Air Pollution on Pulmonary Disease Incidence Analyzed Based on Grey Correlation Analysis

Jiao

Gong

Wang

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…This can include biometric data, symptoms, lifestyle choices, and other healthrelated information. PGHD is increasingly recognized for its potential to improve care and research, particularly in chronic disease management, mental health, and preventive care [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] [8]. Clinical data, on the other hand, refers to information collected by healthcare professionals in the course of providing care.…”

Section: Pghd Understandingmentioning

confidence: 99%

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

Khatiwada,

Yang,

Lin

et al. 2024

JPM

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The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis.

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“…As we can see, classification is the most useful approach for daa analysis. The air quality monitoring system measures the concentration of a certain pollutant in the surrounding and outside environment to systematically monitor the level of pollutants in the air [10] . The classification approach is very useful and adds more value to data.…”

Section: Data Science and Intelligent Computing Techniquesmentioning

confidence: 99%

Drugs Toxicity Prediction: A Review

Kamal¹,

Kumar²,

Sharma³

2023

Data Science and Intelligent Computing Techniques

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Various possible anti-addiction treatments are concerned about drug toxicity, which additionally greatly raises that price of drug research. Drug toxicity can be extremely harmful to the body, resulting in a wide range of diseases such as memory loss, blurriness, liver damage, and so on. Machine learning is an important aspect of drug toxicity; this method helps generate data for many drug candidates. Machine learning also helps in the pharmaceutical industry with quick decisions and critical analyses. Predicting drug toxicity at an early stage has the potential to change people’s lives. During the development of any prediction, safety is critical, as is measuring all effects. We review the various predictive models for predicting the toxicity level, as well as other machine learning techniques that aid in the efficient construction of the predictive model, such as feature selection, balancing, dealing with missing values, and so on, in this paper. Machine learning models along with their approaches such as predictive modeling, ensembling, feature selection, missing values and balancing are used for prediction, This paper focuses on all of the techniques required for predictive modelling.

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Air quality monitoring for Smart eHealth system using firefly optimization and support vector machine

Cited by 34 publications

References 18 publications

[Retracted] The Influence of Air Pollution on Pulmonary Disease Incidence Analyzed Based on Grey Correlation Analysis

[Retracted] The Influence of Air Pollution on Pulmonary Disease Incidence Analyzed Based on Grey Correlation Analysis

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

Drugs Toxicity Prediction: A Review

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