3 research outputs found
Health Care Automation in Compliance to Industry 4.0 Standards: A Case Study of Liver Disease Prediction
No full text263-268The industrial internet contributes to the standards of Industry 4.0, which involve handling large volumes of data using
advanced soft-computing techniques. Machine Learning (ML) is an advanced soft-computing technique that plays a critical
role in predicting and detecting serial chronic diseases, thereby automating the diagnosis. The process constitutes and uses
several data mining algorithms and methods for efficient medical data analysis. Recent studies on several chronic diseases,
liver disorders and diseases associated with the organ have been fatal. In this paper, the liver patient dataset from India is
considered and investigated for developing a classification model. Liver disease is a dangerous, life-threatening disease
often diagnosed false positive. Mild liver enlargement, improper or ambiguous functionality over a brief period, is
prominent even in healthy people, which has become the main reason for ignoring the same at the early stage. It is essential
to predict liver disease through the parameters and their values from the liver functionality test sensing the behavior of
similar patients who were ignored in the initial stage. In this paper, the machine learning technique is demonstrated to
predict liver disease using the liver function test data of the 580 patients as training data. The model has been developed
with an accuracy of approximately 75%. The simulation-based experiment is based on the publicly available dataset and can
be extended to any native set to predict the patients' health quickly. The Random Forest Algorithm is used to develop the
model in Matlab, and the analysis is carried out using parameters like total bilirubin, alkaline phosphotase, alamine
aminotransferase, total proteins, and A/G ratio
Health Care Automation in Compliance to Industry 4.0 Standards: A Case Study of Liver Disease Prediction
Get PDFThe industrial internet contributes to the standards of Industry 4.0, which involve handling large volumes of data using advanced soft-computing techniques. Machine Learning (ML) is an advanced soft-computing technique that plays a critical role in predicting and detecting serial chronic diseases, thereby automating the diagnosis. The process constitutes and uses several data mining algorithms and methods for efficient medical data analysis. Recent studies on several chronic diseases, liver disorders and diseases associated with the organ have been fatal. In this paper, the liver patient dataset from India is considered and investigated for developing a classification model. Liver disease is a dangerous, life-threatening disease often diagnosed false positive. Mild liver enlargement, improper or ambiguous functionality over a brief period, is prominent even in healthy people, which has become the main reason for ignoring the same at the early stage. It is essential to predict liver disease through the parameters and their values from the liver functionality test sensing the behavior of similar patients who were ignored in the initial stage. In this paper, the machine learning technique is demonstrated to predict liver disease using the liver function test data of the 580 patients as training data. The model has been developed with an accuracy of approximately 75%. The simulation-based experiment is based on the publicly available dataset and can be extended to any native set to predict the patients' health quickly. The Random Forest Algorithm is used to develop the model in Matlab, and the analysis is carried out using parameters like total bilirubin, alkaline phosphotase, alamine aminotransferase, total proteins, and A/G ratio
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
Get PDFAbstract
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries
