A decision support system based on support vector machine for diagnosis of periodontal disease

Farhadian, Maryam; Shokouhi, Parisa; Torkzaban, Parviz

doi:10.1186/s13104-020-05180-5

Cited by 42 publications

(25 citation statements)

References 21 publications

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“…explored in literature. [123][124][125][126][127] Using SVMs and clinical variables to detect periodontal diseases, an accuracy of 88.7% was reached in a 10-fold cross-validation (CV) using 300 samples. 124 A biomarker comparison between gingivitis and periodontitis using salivary gene expression profiles, reached an accuracy of 78%.…”

Section: The Use Of These Techniques In Combination Of Different Data...mentioning

confidence: 99%

Applications of artificial intelligence in dentistry: A comprehensive review

Carrillo-Pérez

Pecho

Morales

et al. 2021

J Esthet Restor Dent

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Objective To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions The insight provided by the present work has reported outstanding results in the design of high‐performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

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Section: The Use Of These Techniques In Combination Of Different Data...mentioning

confidence: 99%

Applications of artificial intelligence in dentistry: A comprehensive review

Carrillo-Pérez

Pecho

Morales

et al. 2021

J Esthet Restor Dent

View full text Add to dashboard Cite

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“…Assuming the classes are linearly separable, they obtain the hyper-planes with maximum margin to separate them (Chu & Wang, 2005;El-Gamal et al, 2018;Gayathri, Sumathi & Santhanam, 2013;Karthik & Sudha, 2018;Farhadian, Shokouhi & Torkzaban, 2020).…”

Section: Specificitymentioning

confidence: 99%

“…Not only separating between classes is the primary goal of the optimal hyperplane, but it also increases the margin. Margin is the longest distance between the hyperplane and the closest data (support vector) in each category (Chu & Wang, 2005;Karthik & Sudha, 2018;Farhadian, Shokouhi & Torkzaban, 2020).…”

Section: Hussain Et Al (2019)mentioning

confidence: 99%

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

Alarabi

El–Sappagh

et al. 2021

PeerJ Computer Science

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Background and Objectives This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain’s genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer’s disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. Methods We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. Results It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. Conclusions Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

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“…It is a tree-like structure based on the input features. It is a type of system that has only conditional control [29].…”

Section: Decision Tree Algorithmmentioning

confidence: 99%

Machine Learning-based Web Application for Early Diagnosis of Diabetes

Shahwani

2020

Journal of Applied and Emerging Sciences

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Diabetes has become a chronic disease that seriously threatens human health. It is a group of metabolic diseases characterized by hyperglycemia and there is no role of the age factor involved. The long-term of diabetes disease causes chronic damage and dysfunction of various tissues, especially the eyes, kidneys, heart, blood vessels, and nerves. Most of the time people are not sure about this common disease at the early stage and unluckily the patient moves to a critical situation to meet with major disease due to the continuous effect of diabetes. This research is conducted to build the machine learning-based web application platform for the early diagnosis of the disease, freely accessible anywhere anytime. We used the benchmark dataset named PIDD (Prima Indian Diabetes Dataset) and performed the comparative analysis among the Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest and Support Vector Machines. Based on the classification performance, we found that SVM performed the best among the pool of mentioned algorithms and, therefore, adopted for the development of the intelligent web application for the diabetes diagnosis.

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A decision support system based on support vector machine for diagnosis of periodontal disease

Cited by 42 publications

References 21 publications

Applications of artificial intelligence in dentistry: A comprehensive review

Applications of artificial intelligence in dentistry: A comprehensive review

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Machine Learning-based Web Application for Early Diagnosis of Diabetes

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