Decision support system based on the support vector machines and the adaptive support vector machines algorithm for solving chest disease diagnosis problems

Yahyaoui, Amani; Yumuşak, Nejat

doi:10.4066/biomedicalresearch.29-17-3594

Cited by 18 publications

(12 citation statements)

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“…In [18,35] mammography images are classified using SVMs for benign and malignant masses, but in [18] ROIs are passed as input to the model. In [2,16,43] chest X-Ray images are used to predict tuberculosis and covid-19 using SVM classifier but do not focus on interpretability.…”

Section: Related Workmentioning

confidence: 99%

Interpreting SVM for medical images using Quadtree

Shukla

Verma

Abhishek

et al. 2020

Multimed Tools Appl

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In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.

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Section: Related Workmentioning

confidence: 99%

Interpreting SVM for medical images using Quadtree

Shukla

Verma

Abhishek

et al. 2020

Multimed Tools Appl

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“…The system showed 78.8% accuracy when non-linear binary SVM was used instead of linear with a high rate of sensitivity. Adaptive SVM was commissioned for the first time to diagnose chest disease with high precision value by computing the appropriate bias term value to SVM [66]. In [58] a support vector machine and radial base function network structure is presented to predict the heart disease in the patients but a uni classifier is used in the research.…”

Section: B Support Vector Machine (Svm)mentioning

confidence: 99%

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja¹,

Asghar²

2020

IJACSA

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Clinical advancements are one of the major outcomes of the technological phase shift of data sciences. The significance of information technology in medical sciences by utilizing the Clinical Decision Support System (CDSS) has opened the spillways of exponentially improved predictive models. Utilizing the latest norms of classification algorithms on clinical data are widely incorporated for prognostic assessments. Medical experts have to make decisions that are crucial in nature and if the research can develop a mechanism that assists them in evolving solid reasoning, infer the knowledge and clearly express their clinical decision by justifying their assertions made, it will be a win-win situation. However, this field of science is still an unknown world for clinicians despite the fact that the enormous amount of medical data cannot be exploited to its maximum without invoking the technological support. The objective of this research is to introduce the clinicians and policymakers of the medical domain with the renowned computer-based methodologies employed to construct a clinical decision support system. We expect that gaining the technical insight into the medical domain by the stakeholders will ensure commissioning the accurate and effective CDSS for improved healthcare delivery.

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“…SVMs are well-known supervised classification algorithms that separate various groups of data [27], [28]. These vectors are grouped by optimizing a specific line so that the neighboring point in each group will be the farthest away from each other.…”

Section: Svmmentioning

confidence: 99%

An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance

Ahmed

Hussien

2019

UHD J SCI TECH

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The future of healthcare may look completely different from the current clinic-center services. Rapidly growing and developing technologies are expected to change clinics throughout the world. However, the healthcare delivered to impaired patients, such as elderly and disabled people, possibly still requires hands-on human expertise. The aim of this study is to propose a predictive model that pre-diagnose illnesses by analyzing symptoms that are interactively taken from patients via several hand gestures during a period of time. This is particularly helpful in assisting clinicians and doctors to gain better understanding and make more accurate decisions about future plans for their patients’ situations. The hand gestures are detected, the time of the gesture is recorded and then they are associated to their designated symptoms. This information is captured in the form of provenance graphs constructed based on the W3C PROV data model. The provenance graph is analyzed by extracting several network metrics and then supervised machine-learning algorithms are used to build a predictive model. The model is used to predict diseases from the symptoms with a maximum accuracy of 84.5%.

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Decision support system based on the support vector machines and the adaptive support vector machines algorithm for solving chest disease diagnosis problems

Cited by 18 publications

References 0 publications

Interpreting SVM for medical images using Quadtree

Interpreting SVM for medical images using Quadtree

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance

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