ArticleRisk management and healthcare policy2020
Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective.
Article in Risk management and healthcare policy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review.Journal of medical Internet research · 2026Article
- Web application development using multiple regression analysis to predict silicosis risk among stone carvers in Nakhon Ratchasima, Thailand.Journal of occupational medicine and toxicology (London, England) · 2026Article
- Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology.Scientific reports · 2024Article
- Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- Analysis of the Exploration of Security and Privacy for Healthcare Management Using Artificial Intelligence: Saudi Hospitals.Computational intelligence and neuroscience · 2022Article
- Trustworthy Intrusion Detection in E-Healthcare Systems.Frontiers in public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe imperative need for ensuring optimal security of healthcare web applications cannot be overstated. Security practitioners are consistently working at improvising on techniques to maximise security along with the longevity of healthcare web applications. In this league, it has been observed that assessment of security risks through soft computing techniques during the development of web application can enhance the security of healthcare web applications to a great extent.
methodsThis study proposes the identification of security risks and their assessment during the development of the web application through adaptive neuro-fuzzy inference system (ANFIS). In this article, firstly, the security risk factors involved during healthcare web application development have been identified. Thereafter, these security risks have been evaluated by using the ANFIS technique. This research also proposes a fuzzy regression model.
resultsThe results have been compared with those of ANFIS, and the ANFIS model is found to be more acceptable for the estimation of security risks during the healthcare web application development.
conclusionThe proposed approach can be applied by the healthcare web application developers and experts to avoid the security risk factors during healthcare web application development for enhancing the healthcare data security.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.