scholarly journals Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Hatice Kök ◽  
Ayse Merve Acilar ◽  
Mehmet Said İzgi
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Growth And Development ◽  
Cervical Vertebrae ◽  
Reference Points ◽  
Support Vector ◽  
Linear Measurements ◽  
K Nearest Neighbors ◽  
Stable Algorithm ◽  
Average Rank

Abstract Background Growth and development can be determined by cervical vertebrae stages that were defined on the cephalometric radiograph. Artificial intelligence has the ability to perform a variety of activities, such as prediction-classification in many areas of life, by using different algorithms, In this study, we aimed to determine cervical vertebrae stages (CVS) for growth and development periods by the frequently used seven artificial intelligence classifiers, and to compare the performance of these algorithms with each other. Methods Cephalometric radiographs, that were obtained from 300 individuals aged between 8 and 17 years were included in our study. Nineteen reference points were defined on second, third, and 4th cervical vertebrae, and 20 different linear measurements were taken. Seven algorithms of artificial intelligence that are frequently used in the field of classification were selected and compared. These algorithms are k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (Tree), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and logistic regression (Log.Regr.) algorithms. Results According to confusion matrices decision tree, CSV1 (97.1%)–CSV2 (90.5%), SVM: CVS3 (73.2%)–CVS4 (58.5%), and kNN: CVS 5 (60.9%)–CVS 6 (78.7%) were the algorithms with the highest accuracy in determining cervical vertebrae stages. The ANN algorithm was observed to have the second-highest accuracy values (93%, 89.7%, 68.8%, 55.6%, and 78%, respectively) in determining all stages except CVS5 (47.4% third highest accuracy value). According to the average rank of the algorithms in predicting the CSV classes, ANN was the most stable algorithm with its 2.17 average rank. Conclusion In our experimental study, kNN and Log.Regr. algorithms had the lowest accuracy values. SVM-RF-Tree and NB algorithms had varying accuracy values. ANN could be the preferred method for determining CVS.

scholarly journals Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models

2020 ◽  
Vol 49 (5) ◽  
pp. 20190441 ◽  
Author(s):  
Hakan Amasya ◽  
Derya Yildirim ◽  
Turgay Aydogan ◽  
Nazan Kemaloglu ◽  
Kaan Orhan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cervical Vertebrae ◽  
Clinical Decision ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Human Observer ◽  
Ann Model ◽  
Visual Evaluation ◽  
Cervical Vertebral Maturation

Objectives: This study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical decision support system (CDSS) was developed for more objective results. Methods: A total of 647 digital lateral cephalometric radiographs with visible C2, C3, C4 and C5 vertebrae were chosen. Newly developed software was used for manually labelling the samples, with the integrated CDSS developed by evaluation of 100 radiographs. On each radiograph, 26 points were marked, and the CDSS generated a suggestion according to the points and CVM analysis performed by the human observer. For each sample, 54 features were saved in text format and classified using logistic regression (LR), support vector machine, random forest, artificial neural network (ANN) and decision tree (DT) models. The weighted κ coefficient was used to evaluate the concordance of classification and expert visual evaluation results. Results: Among the CVM stage classifier models, the best result was achieved using the ANN model (κ = 0.926). Among cervical vertebrae morphology classifier models, the best result was achieved using the LR model (κ = 0.968) for the presence of concavity, and the DT model (κ = 0.949) for vertebral body shapes. Conclusions: This study has proposed ML models for CVM assessment on lateral cephalometric radiographs, which can be used for the prediction of cervical vertebrae morphology. Further studies should be done especially of forensic applications of AI models through CVM evaluations.

scholarly journals A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

2020 ◽  
Author(s):  
Mohamed El Boujnouni
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The World ◽  
Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.

scholarly journals Applications of artificial intelligence and machine learning in orthodontics: a scoping review

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yashodhan M. Bichu ◽  
Ismaeel Hansa ◽  
Aditi Y. Bichu ◽  
Pratik Premjani ◽  
Carlos Flores-Mir ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Treatment Planning ◽  
Scoping Review ◽  
Growth And Development ◽  
Diagnosis And Treatment ◽  
Support Vector ◽  
Landmark Detection ◽  
Anatomic Landmark

Abstract Introduction This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application. Methods A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020. Results Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning—either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein. Conclusion This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment.

scholarly journals Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence

2021 ◽  
Vol 11 (8) ◽  
pp. 803
Author(s):  
Eva M. Sánchez-Morla ◽  
Juan L. Fuentes ◽  
Juan M. Miguel-Jiménez ◽  
Luciano Boquete ◽  
Miguel Ortiz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bipolar Disorder ◽  
Optical Coherence Tomography ◽  
Feature Vector ◽  
High Capacity ◽  
Area Under The Curve ◽  
Support Vector ◽  
Type I ◽  
Optical Coherence ◽  
K Nearest Neighbors

Background: The aim of this study is to explore an objective approach that aids the diagnosis of bipolar disorder (BD), based on optical coherence tomography (OCT) data which are analyzed using artificial intelligence. Methods: Structural analyses of nine layers of the retina were analyzed in 17 type I BD patients and 42 controls, according to the areas defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The most discriminating variables made up the feature vector of several automatic classifiers: Gaussian Naive Bayes, K-nearest neighbors and support vector machines. Results: BD patients presented retinal thinning affecting most layers, compared to controls. The retinal thickness of the parafoveolar area showed a high capacity to discriminate BD subjects from healthy individuals, specifically for the ganglion cell (area under the curve (AUC) = 0.82) and internal plexiform (AUC = 0.83) layers. The best classifier showed an accuracy of 0.95 for classifying BD versus controls, using as variables of the feature vector the IPL (inner nasal region) and the INL (outer nasal and inner inferior regions) thickness. Conclusions: Our patients with BD present structural alterations in the retina, and artificial intelligence seem to be a useful tool in BD diagnosis, but larger studies are needed to confirm our findings.

scholarly journals Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

2019 ◽  
pp. 131-136
Author(s):  
Umar Sidiq ◽  
Syed Mutahar Aaqib ◽  
Rafi Ahmad Khan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Research Work ◽  
Support Vector ◽  
Data Sets ◽  
Data Mining Technique ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Classification Techniques ◽  
Using Data

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

scholarly journals Proposing a framework for specifying the appropriateness of pursuing a career regarding Employees’ healthiness and safeguard: A Case study of food manufacturing industry

2021 ◽  
Author(s):  
Ayria Behdinian ◽  
Kamran Rezaie ◽  
Ali Bozorgi-Amiri
Keyword(s):  
Decision Tree ◽  
Manufacturing Industry ◽  
Confusion Matrix ◽  
Well Being ◽  
Functional System ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Food Manufacturing ◽  
Human Resources Managers ◽  
Job Position

Abstract BackgroundEmployee health is an essential issue for Human Resource Management (HRM). The employees' health level is undeniably correlated to the job position in which they work since it may harm their well-being, and they may not be capable of performing their duties properly. Prompt diagnosis and resolution of employees' physical complications are highly critical.MethodsMachine learning (ML) is the state-of-the-art method potentially utilized to make early predictions to safeguard employees' healthiness. The technical laborers within the food manufacturing company are included in this Research. The functional classification models, namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree, are exploited to predict the employees' wellness for their vocation. K-fold Cross-Validation (KCV) and Confusion Matrix were applied in this study, the former for estimating the model's functionality and the latter for forecasting accuracy.ResultsAfter implementing four models on the 231 employees, the accuracy was extracted out, SVM with 78%, KNN with 78%, Decision Tree with 80%, and the highest for LR algorithm with 84%.ConclusionsIn this Research, the LR algorithm was opted to paving the way for Human Resources Managers in order to utilize a functional system to predict the Suitability of factory workers concerning their healthiness. The Hearing condition was picked out as a leading factor in selecting employees for their job position. Consequently, it is significant to planning a hearing conservation program for employees, especially those exposed to excessive noise.Trial Registration: Retrospectively registered.

scholarly journals Avaliação de técnicas de inteligência computacional para identificação de atividades de vida diária

2018 ◽  
Author(s):  
Wylken S. Machado ◽  
Pedro H. Barros ◽  
Eliana S. Almeida ◽  
Andre L. L. Aquino
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbors ◽  
Low Energy ◽  
Support Vector ◽  
Body Sensor Networks ◽  
K Nearest Neighbors

Neste trabalho apresentamos a avaliação do desempenho de algoritmos de machine learning para identificar Atividades de Vida Diária (ADLs) e quedas. Nós avaliamos os seguintes algoritmos: K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra-Trees e Redes Neurais Recorrentes. Utilizamos um conjunto de dados coletados por uma Body Sensor Networks com cinco dispositivos sensores conectados através da interface Bluetooth Low Energy, chamado UMAFall. Obtivemos resultados satisfatórios, principalmente para as atividades saltar e queda frontal, com 100 % de acurácia, utilizando o algoritmo Extra-Trees.

scholarly journals IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1613
Author(s):  
Francisco A. da S. Freitas ◽  
Francisco F. X. Vasconcelos ◽  
Solon A. Peixoto ◽  
Mohammad Mehedi Hassan ◽  
M. Ali Akber Dewan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
School Dropout ◽  
Educational Process ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Flexible Tool ◽  
New Students ◽  
Socioeconomic Data

School dropout permeates various teaching modalities and has generated social, economic, political, and academic damage to those involved in the educational process. Evasion data in higher education courses show the pessimistic scenario of fragility that configures education, mainly in underdeveloped countries. In this context, this paper presents an Internet of Things (IoT) framework for predicting dropout using machine learning methods such as Decision Tree, Logistic Regression, Support Vector Machine, K-nearest neighbors, Multilayer perceptron, and Deep Learning based on socioeconomic data. With the use of socioeconomic data, it is possible to identify in the act of pre-registration who are the students likely to evade, since this information is filled in the pre-registration form. This paper proposes the automation of the prediction process by a method capable of obtaining information that would be difficult and time consuming for humans to obtain, contributing to a more accurate prediction. With the advent of IoT, it is possible to create a highly efficient and flexible tool for improving management and service-related issues, which can provide a prediction of dropout of new students entering higher-level courses, allowing personalized follow-up to students to reverse a possible dropout. The approach was validated by analyzing the accuracy, F1 score, recall, and precision parameters. The results showed that the developed system obtained 99.34% accuracy, 99.34% F1 score, 100% recall, and 98.69% precision using Decision Tree. Thus, the developed system presents itself as a viable option for use in universities to predict students likely to leave university.

scholarly journals A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 635 ◽  
Author(s):  
Shaker El-Sappagh ◽  
Mohammed Elmogy ◽  
Farman Ali ◽  
Tamer ABUHMED ◽  
S. M. Riazul Islam ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Tree ◽  
Ensemble Classifier ◽  
Heterogeneous Data ◽  
Classification Performance ◽  
Medical Decision ◽  
Support Vector ◽  
University Hospitals ◽  
K Nearest Neighbors ◽  
Medical Decision Support

Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

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