Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm

Devikanniga, D.; Ramu, Arulmurugan; Haldorai, Anandakumar

doi:10.4108/eai.13-7-2018.164177

Cited by 146 publications

(24 citation statements)

References 19 publications

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“…SVM maps the given data into linearly separable and non linearly separable data. If the given data can be separated linearly SVM can easily separate two classes (Devikanniga, Ramu, and Haldorai 2018). If the given data is non-linearly separable then, the datas are mapped into higher dimensions to provide better classification performance.…”

Section: Methodsmentioning

confidence: 99%

Detection of Liver Disorder Using RBF SVM in Comparison with Naïve Bayes to Measure the Accuracy, Precision, Sensitivity and Specificity

Madhu¹,

Ganapathy²

2021

alinteri

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Aim: Machine learning techniques are rapidly used in the area of medical research due to its impressive results in diagnosis and prediction of diseases. The objective of this study is to evaluate the performance of SVM classifier in identification of liver disorder by comparing it with Naive Bayes algorithm. Methods and Materials: A total of 31619 samples are collected from three liver disease datasets available in kaggle. These samples are divided into training dataset (n = 22133 [70%]) and test dataset (n = 9486 [30%]). Accuracy, precision, specificity and sensitivity values are calculated to quantify the performance of the SVM algorithm. Results: SVM achieved accuracy, precision, sensitivity and specificity of 73.64%, 97.82%, 97.56% and 69.77% respectively compared to 57.31%, 41.39%, 94.87% and 37.20% by Naive Bayes algorithm. Conclusion: In this study it is found that the RBF SVM algorithm performed better than the Naive Bayes algorithm in liver disorder detection of the datasets considered.

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Section: Methodsmentioning

confidence: 99%

Detection of Liver Disorder Using RBF SVM in Comparison with Naïve Bayes to Measure the Accuracy, Precision, Sensitivity and Specificity

Madhu¹,

Ganapathy²

2021

alinteri

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“…The section at beginning built as testing the system by [11] interval database, when the image determined before enhancement and then measure the segmentation and hide text rate of the system when the determination process performed after enhancement the images, figure1 showed the sample of the database in this paper [12].…”

Section: Statistical Proposed Methodology Approachmentioning

confidence: 99%

Text Steganography in Statistically Clustered Iris Image

Neamah¹,

Mohammed²

2018

EAI Endorsed Transactions on Energy Web

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“…Regardless of the fact that workflow interruptions and time sensitivity (high workloads) are common in all hospital environments, the requirement to evaluate clinical data in a brief consultation may contribute to CDSS deployment being restricted in primary care. In [10], researchers' results demonstrate how mediating variable factors like age, clinical experience, and ehealth literacy influence clinicians' behavior when it comes to implementing digital healthcare systems. These results are in line with previous research on the impact of these variables on the perceived utility of digital health systems and users' intentions to utilize them.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

2021

JBSHA

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The growing availability of medical data has sparked fresh interests in Computerized Clinical Decision Support Systems (CDSS), thanks to recent breakthroughs in machine and deep learning. CDSS has showed a lot of promise in terms of improving healthcare, enhancing the safety of patients and minimizing treatment costs. The application of CDSS, nonetheless, is unsafe since an insufficient or defective CDSS may possibly degrade healthcare quality and place patients at potential threat. Furthermore, the deployment of a CDSS may fail when the CDSS's output is ignored by its intended users owing to a lack of confidence, relevance, or actionability. We offer literature-based advice for the various elements of CDSS adoption, with a particular emphasis on Artificial Intelligence (AI) and Machine Learning (ML) systems: quality assurance, deployment, commissioning, acceptability tests, and selection, in this research. A critical selection process will assist in the process of identifying CDSS, which effectively suits the localized sites’ needs and preferences. Acceptance testing ensures that the chosen CDSS meets the specified standards and meets the safety criteria. The CDSS will be ready for safe clinical usage at the local site once the commissioning procedure is completed. An efficient system implementation must result in a smooth rollout of the CDSS to well-trained end-users with reasonable expectations. Furthermore, quality assurance will ensure that the CDSS's levels are maintained and that any problems are discovered and resolved quickly. We conclude this research by discussing the methodical adoption process for CDSS to assist in avoiding issues, enhance the safety of patients and increasing quality of service.

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Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm

Cited by 146 publications

References 19 publications

Detection of Liver Disorder Using RBF SVM in Comparison with Naïve Bayes to Measure the Accuracy, Precision, Sensitivity and Specificity

Detection of Liver Disorder Using RBF SVM in Comparison with Naïve Bayes to Measure the Accuracy, Precision, Sensitivity and Specificity

Text Steganography in Statistically Clustered Iris Image

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

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