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Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

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BMC ORAL HEALTH 2024年第1期24卷 1页

作者： Ciftci, Burak Tunahan Asantogrol, Firdevs Gaziantep Univ Fac Dent Dept Oral & Maxillofacial Surg TR-27310 Gaziantep Turkiye

Background The aim of this study was to analyse the risk factors that affect oral health in adults and to evaluate the success of different machine learning algorithms in predicting these risk factors. Methods This study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological examinations were performed, and DMFT scores were calculated after completion of the questionnaire. The obtained data were randomly divided into a 75% training group and a 25% test group. The preprocessed dataset was analysed using various machine learning algorithms, including naive Bayes, logistic regression, support vector machine, decision tree, random forest and Multilayer Perceptron algorithms. Pearson's correlation test was also conducted to assess the correlation between participants' DMFT scores and oral health risk factors. The performance of each algorithm was evaluated to determine the most appropriate algorithm, and model performance was assessed using accuracy, precision, recall and F1 score on the test dataset. Results A statistically significant difference was found between various factors and DMFT-based risk groups (p < 0.05), including age, sex, body mass index, tooth brushing frequency, socioeconomic status, employment status, education level, marital status, hypertension, diabetes status, renal disease status, consumption of sugary snacks, dry mouth status and screen time. When considering machine learning algorithms for risk group assessments, the Multilayer Perceptron model demonstrated the highest level of success, achieving an accuracy of 95.8%, an F1-score of 96%, and precision and recall rates of 96%. Conclusions Caries risk assessment using a simple quest

关键词： Oral health Dental caries susceptibility machine learning algorithms Prediction

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machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma

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BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE 2024年第7期1870卷 167357页

作者： Abedi, Samira Behmanesh, Ali Mazhar, Farid Najd Bagherifard, Abolfazl Sami, Sam Hajialiloo Heidari, Negar Hossein-Khannazer, Nikoo Namazifard, Saina Arki, Mandana Kazem Shams, Roshanak Zarrabi, Ali Vosough, Massoud Univ Sci & Culture Fac Sci & Adv Technol Biol Dept Cellular & Mol Biol Tehran Iran Iran Univ Med Sci Bone & Joint Reconstruct Res Ctr Sch Med Dept Orthoped Tehran Iran Shahid Beheshti Univ Med Sci Res Inst Gastroenterol & Liver Dis Gastroenterol & Liver Dis Res Ctr Tehran Iran Univ Texas Arlington Dept Mech & Aerosp Engn Arlington TX USA ACECR Dept Regenerat Med Royan Inst Stem Cell Biol & Technol Cell Sci Res Ctr Tehran Iran Istinye Univ Fac Engn & Nat Sci Dept Biomed Engn TR-34396 Istanbul Turkiye Yuan Ze Univ Grad Sch Biotechnol & Bioengn Taoyuan 320315 Taiwan Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Dent Coll & Hosp Dept Res Analyt Chennai 600077 India Karolinska Inst Dept Lab Med Expt Canc Med Stockholm Sweden Shahid Beheshti Univ Med Sci Sch Adv Technol Med Dept Tissue Engn & Appl Cell Sci Tehran Iran

Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosarcoma metastasis. In the first step, we used RNA-seq and online microarray data from osteosarcoma tissues and cell lines to identify differentially expressed miRNAs. Then, using seven feature selection algorithms for ranking, the first-ranked miRNAs were selected as input for five machine learning systems. Using network analysis and machine learning algorithms, we developed new diagnostic models that successfully differentiated metastatic osteosarcoma from non-metastatic samples based on newly discovered miRNA signatures. The results showed that miR-34c-3p and miR-154-3p act as the most promising models in the diagnosis of metastatic osteosarcoma. Validation for this model by RT-qPCR in benign tissue and osteosarcoma biopsies confirmed the lower expression of miR-34c-3p and miR-154-3p in OS samples. In addition, a direct correlation between miR-34c-3p expression, miR-154-3p expression and tumor grade was discovered. The combined values of miR-34c-3p and miR-154-3p showed 90 % diagnostic power (AUC = 0.90) for osteosarcoma samples and 85 % (AUC = 0.85) for metastatic osteosarcoma. Adhesion junction and focal adhesion pathways, as well as epithelial-to-mesenchymal transition (EMT) GO terms, were identified as the most significant KEGG and GO terms for the top miRNAs. The findings of this study highlight the potential use of novel miRNA expression signatures for early detection of metastatic osteosarcoma. These findings may help in determining therapeutic approaches with a quantitative and faster method of metastasis detection and also be used in the development of targeted molecular therapy for this aggressive cancer. Further research is needed to confirm the clinical utility of miR-34c-3p and miR-154-3p as diagnostic biom

关键词： Metastatic osteosarcoma Early biomarkers machine learning algorithms Differentially expressed miRNAs

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Smart solutions for dissolved oxygen control in semi-batch fermenters: A machine learning approach

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DESALINATION AND WATER TREATMENT 2024年 317卷

作者： Sumathi, J. Aravind, P. Gandhimathi, G. Inst Engineers India IEI Thanjavur Tamil Nadu India Mattu Univ Dept Elect & Comp Engn Mattu Ethiopia SRM TRP Engn Coll Dept Elect & Commun Engn Tiruchirappalli Tamil Nadu India

The monitoring and measurement of dissolved oxygen (DO), plays a significant role in industrial effluent, and disposal of effluents has been a serious challenge to researchers. Aeration is the primary step in effluent treatment. The basic method for determining DO is direct measurement. This study has been carried out in effluent from the paper industry to estimate the performance of dissolved oxygen in a semi-batch lark hygiene fermenter and was oxygenated till saturation at 25 degrees C under real-time study. This study has been carried out using MultiLayered Feed Forward Back Propagation Artificial Neural Network (ML-FFBPANN) and the experimental results were optimized with fourteen different machine learning algorithms such as Polak-Ribiere Conjugate Gradient (CGP), Conjugate Gradient with Powell/Beale Restarts (CGB), Bayesian Regularization (BR), Gradient Descent (GD), BFGS Quasi-Newton (BFG), GD with Momentum (GDM), Gaussian Discriminate Analysis(GDA), FletcherPowell Conjugate Gradient (CGF), Resilient Backpropagation (RP), Variable learning Rate Gradient Descent (GDX), One Step Secant (OSS), Regression (R), Levenberg-Marquardt (LM), Scaled Conjugate Gradient(SCG). Parameters Root Mean Squared Error (RMSE) and the correlation coefficient (R-square) were used to evaluate the performance of each model and mathematical modeling for correlation co-efficient of all algorithms are also presented. LM and R are the best algorithms but the ML-FFBPANN-LM algorithm provided the best optimization results of maximum correlation co-efficient. LM algorithm produced a maximum correlation coefficient under training (0.99818), testing (0.99351), valediction (1.0), and an overall correlation coefficient be 0.9568. From the study, it can be concluded that the ML-FFBPANN with LM algorithm can be used in the aeration process in effluent treatment for analysis and prediction of process parameters.

关键词： Artificial neural network machine learning algorithms Dissolved oxygen Paper industry effluent Regression analysis Dissolved oxygen parameter Semi-batch fermenter

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Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma

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BMC GASTROENTEROLOGY 2024年第1期24卷 1-10页

作者： Xie, Hui Hong, Tao Liu, Wencai Jia, Xiaodong Wang, Le Zhang, Huan Xu, Chan Zhang, Xiaoke Li, Wen-Le Wang, Quan Yin, Chengliang Lv, Xu Yanan Peoples Hosp Dept Gen Surg Yanan Peoples R China Chinese Acad Med Sci Fuwai Hosp Dept Cardiac Surg Shenzhen Peoples R China Nanchang Univ Affiliated Hosp 1 Dept Orthopaed Surg Nanchang Peoples R China Peoples Liberat Army Gen Hosp Med Ctr 5 Dept Oncol Beijing Peoples R China Dalian Med Univ Affiliated Hosp 1 Dept Thorac Surg Dalian Peoples R China Grad Sch Shaanxi Univ Chinese Med Grad Sch Xianyang 712046 Peoples R China Xiamen Univ Sch Publ Hlth State Key Lab MolecularVaccinol & Mol Diagnost Xiamen 361102 Peoples R China Xiamen Univ Ctr Mol Imaging & Translat Med Sch Publ Hlth Xiamen 361102 Peoples R China Peoples Liberat Army Gen Hosp Med Ctr 5 Radiat Oncol Dept Beijing Peoples R China Macau Univ Sci & Technol Fac Med Taipa Macao Peoples R China Yixing Canc Hosp Dept Gen Surg Yixing 214200 Jiangsu Peoples R China

Objective Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with *** A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing *** In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of *** The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

关键词： Intrahepatic cholangiocarcinoma machine learning algorithms Lymph node metastasis Web calculator

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Advancements in mapping landslide susceptibility in Bafoussam and its surroundings area using multi-criteria decision analysis, statistical methods, and machine learning models

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JOURNAL OF AFRICAN EARTH SCIENCES 2024年 213卷

作者： Segue, Willy Stephane Njilah, Isaac Konfor Fossi, Donald Hermann Nsangou, Daouda Univ Yaounde I Dept Earth Sci POB 812 Yaounde Cameroon Inst Geol & Min Res POB 4110 Yaounde Cameroon

Landslides pose a significant threat to lives and socio-economic stability globally. In this study, we conducted a comprehensive landslide susceptibility mapping (LSM) in the western region of Cameroon, focusing on Bafoussam and its surroundings. The integration of multi-criteria decision analysis models (AHP), statistical methods (Information Value IV, Shannon Entropy SE, Frequency Ratio FR), and machine learning algorithms (Na & iuml;ve Bayes and Logistic Regression) provided a robust assessment of landslide risk. Our analysis, based on 54 recorded landslides, carefully selected Landslide Conditioning Factors (LCF), and influential parameters such as lithology, slope, altitude, and precipitation, resulted in susceptibility maps categorizing the area into five risk zones. The Spatial distribution shows the centre and northwestern regions as high-risk areas. Model sensitivity differences underscore the need for tailored LSM selection. Validation using the Area Under Curve/Receiver Operating Characteristics (AUC/ROC) method indicates the LR and NB methods have the highest accuracy (82.7% and 84.1%, respectively). Comparative analysis of landslide events in Gouache, Sichuan, Souk Ahras, and Kekem reveals correlations between heavy rainfall and geological conditions. The study supplies valuable insights for decision-makers in landslide-prone areas, emphasizing the importance of integrating multiple methodologies for comprehensive risk assessment and management.

关键词： Susceptibility mapping Causative factors machine learning algorithms AHP Bafoussam

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Tribo-informative Analysis of Spark Plasma Sintered Al-SiC-HAp Hybrid Composite Through machine learning Techniques

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-16页

作者： Venkatesh, V. S. S. Rao, R. Narasimha Manohar, Guttikonda Singh, Sapam Ningthemba Indian Inst Technol Bhubaneswar Sch Mech Sci Argul 752050 Odisha India Natl Inst Technol Warangal Warangal 506004 Telangana India Natl Inst Technol Silchar Silchar 788010 Assam India CVR Coll Engn Dept Mech Engn Rangareddy 501510 Telangana India

This research epitomizes the tribological analysis of the SiC and hydroxyapatite (HAp) reinforced aluminium composite. Hydroxyapatite was extracted from the mrigal fish and used as a secondary reinforcement to enhance the tribological characteristics of the aluminium matrix. A DUCOM TR20LE pin-on-disc tribometer and ECONEMET VH1MD hardness were used to assess the wear and hardness of the synthesized spark plasma sintered Al- 10%SiC- x%HAp (x = 0, 2.5, 5, 7.5, 10 and 12.5 wt%) hybrid composite respectively. X-ray diffraction analysis confirmed the presence of HAp and beta-tricalcium phosphate in the mrigal fish bone extracted HAp powder. The OH-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${OH}<^>{-}$$\end{document}, CO32-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${CO}_{3}<^>{2-}$$\end{document} and PO43-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${PO}_{4}<^>{3-}$$\end{document} bands were identified by conducting Fourier transform infrared spectroscopy which confirms the HAp in the calcinated powder. Hardness results reveal that the composite corresponds to the 7.5 wt% HAp achieved the maximum hardness of 129 VHN. The composite with 2.5 wt% reinforced HAp in the Al- 10wt%SiC- HAp composite exhibits the minimum wear and COF of 0.37 mg and 0.3 respectively for 5 N applied load. To investigate the wear behaviour of the synthesized composite, different machine learning techniques such as KNN, SVM, and ANN have been utilized

关键词： Aluminium matrix Hydroxyapatite (HAp) Spark plasma sintering machine learning algorithms Tribological properties

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Analysis of banana plant health using machine learning techniques

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Thiagarajan, Joshva Devadas Kulkarni, Siddharaj Vitthal Jadhav, Shreyas Anil Waghe, Ayush Ashish Raja, S. P. Rajagopal, Sivakumar Poddar, Harshit Subramaniam, Shamala Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India Univ Putra Malaysia Dept Commun Technol & Networks Serdang Selangor Malaysia

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second int

关键词： Banana industry Disease detection Agricultural farming machine learning algorithms Banana leaf diseases Automated systems

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machine learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD)

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JOURNAL OF PROTEOME RESEARCH 2024年第6期23卷 2253-2264页

作者： Li, Xiaocheng Xiao, Yaqing Chen, Xinhuan Zhu, Yayun Du, Haoqi Shu, Jian Yu, Hanjie Ren, Xiameng Zhang, Fan Dang, Jing Zhang, Chen Su, Shi Li, Zheng Northwest Univ Coll Life Sci Lab Funct Glyc Xian 710069 Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 1 Dept Hlth Sci Ctr Xian 710061 Peoples R China Northwest Univ Fac Life Sci & Med Sch Med Xian 710069 Peoples R China

Nonalcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition globally, and underdiagnosis is common, particularly in mild cases, attributed to the asymptomatic nature and traditional ultrasonography's limited sensitivity to detect early-stage steatosis. Consequently, patients may experience progressive liver pathology. The objective of this research is to ascertain the efficacy of serum glycan glycopatterns as a potential diagnostic biomarker, with a particular focus on the disease's early stages. We collected a total of 170 serum samples from volunteers with mild-NAFLD (Mild), severe-NAFLD (Severe), and non-NAFLD (None). Examination via lectin microarrays has uncovered pronounced disparities in serum glycopatterns identified by 19 distinct lectins. Following this, we employed four distinct machine learning algorithms to categorize the None, Mild, and Severe groups, drawing on the alterations observed in serum glycopatterns. The gradient boosting decision tree (GBDT) algorithm outperformed other models in diagnostic accuracy within the validation set, achieving an accuracy rate of 95% in differentiating the None group from the Mild group. Our research indicates that employing lectin microarrays to identify alterations in serum glycopatterns, when integrated with advanced machine learning algorithms, could constitute a promising approach for the diagnosis of NAFLD, with a special emphasis on its early detection.

关键词： nonalcoholic fatty liver disease serum glycopatterns machine learning algorithms

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Securities Quantitative Trading Strategy Based on Deep learning of Industrial Internet of Things

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2024年第1期19卷 1页

作者： Tang, Yi Wang, Xiaoning Wang, Wenyan Chongqing Inst Engn Chongqing Peoples R China Cangzhou Normal Univ Cangzhou Peoples R China

By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function;on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.

关键词： machine learning algorithms Deep learning Quantitative trading of securities RB_DRL algorithm Financial industry Industrial internet of things

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What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? A case-control study in orthodontically treated patients

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PROGRESS IN ORTHODONTICS 2024年第1期25卷 31页

作者： Alhazmi, Nora Alaqla, Ali Almuzzaini, Bader Aldrees, Mohammed Alnaqa, Ghaida Almasoud, Farah Aldibasi, Omar Alshamlan, Hala King Saud bin Abdulaziz Univ Hlth Sci Coll DentMinist Natl Guard Hlth Affairs King Abdullah Int Med Res Ctr Dept Prevent Dent Sci Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci Coll DentMinist Natl Guard Hlth Affairs King Abdullah Int Med Res Ctr Dept Restorat & Prosthet Dent Sci Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci King Abdullah Int Med Res Ctr Dept Med Genom Res Minist Natl Guard Hlth Affairs Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci Coll Dent King Abdullah Int Med Res Ctr Minist Natl Guard Hlth Affairs Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci King Abdullah Int Med Res Ctr Biostat Sect Minist Natl Guard Hlth Affairs Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh Saudi Arabia

Background Hypodontia is the most prevalent dental anomaly in humans, and is primarily attributed to genetic factors. Although genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNP) associated with hypodontia, genetic risk assessment remains challenging due to population-specific SNP variants. Therefore, we aimed to conducted a genetic analysis and developed a machine-learning-based predictive model to examine the association between previously reported SNPs and hypodontia in the Saudi Arabian population. Our case-control study included 106 participants (aged 8-50 years;64 females and 42 males), comprising 54 hypodontia cases and 52 controls. We utilized TaqMan (TM) Real-Time Polymerase Chain Reaction and allelic genotyping to analyze three selected SNPs (AXIN2: rs2240308, PAX9: rs61754301, and MSX1: rs12532) in unstimulated whole saliva samples. The chi-square test, multinomial logistic regression, and machine-learning techniques were used to assess genetic risk by using odds ratios (ORs) for multiple target variables. Results Multivariate logistic regression indicated a significant association between homozygous AXIN2 rs2240308 and the hypodontia phenotype (ORs [95% confidence interval] 2.893 [1.28-6.53]). machine-learning algorithms revealed that the AXIN2 homozygous (A/A) genotype is a genetic risk factor for hypodontia of teeth #12, #22, and #35, whereas the AXIN2 homozygous (G/G) genotype increases the risk for hypodontia of teeth #22, #35, and #45. The PAX9 homozygous (C/C) genotype is associated with an increased risk for hypodontia of teeth #22 and #35. Conclusions Our study confirms a link between AXIN2 and hypodontia in Saudi orthodontic patients and suggests that combining machine-learning models with SNP analysis of saliva samples can effectively identify individuals with non-syndromic hypodontia.

关键词： machine learning algorithms Non-syndromic hypodontia Single nucleotide polymorphism AXIN2 PAX9 MSX1 Genetics Orthodontics

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