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Development of feline infectious peritonitis diagnosis system by using CatBoost algorithm.

This study employed machine learning techniques to predict the rate of feline infectious peritonitis (FIP) diagnoses, with a specific focus … curate predictive models, namely logistic regression, random forest, decision tree, neural network, support vector machine, gradient boosting tree, and categorical boosting (CatBoost) algorithms. The model obtained using the CatBoost algorithm was discovered to have accuracy of 0.9541. Accordingly, a highly accurate predictive model was developed to enable early diagnos …

Applicability of the Suchey-Brooks method for age estimation in an Indian population: A computed tomography-based exploration using Bayesian analysis and machine learning.

Age estimation occupies a prominent niche in the identification process. In cases where skeletal remains present for examination, age is often estimated from markers distributed throughout the skeletal framework. Within the pelvis, the pubic symphysis constitutes one of the more commonly utilized skeletal markers for age estimation, with the Suchey-Brooks method comprising one of the more commonly employed methods for pubic symphyseal age estimation. The present study was targeted towards assessing the applicability of the Suchey-Brooks method for pubic symphyseal age estimation, an aspect lar

A preliminary study in classification of the severity of spine deformation in adolescents with lumbar/thoracolumbar idiopathic scoliosis using machine learning algorithms based on lumbosacral joint efforts during gait.

To assess the severity and progression of adolescents with idiopathic scoliosis (AIS), radiography with X-rays is usually used. The methods … is i.e. mild, moderate and severe. The ensemble classifier algorithm including k-nearest neighbors, support vector machine, random forest and multilayer perceptron achieved the most promising results, with accuracy scores of 91.4%. This preliminary study shows lumbosacral joint efforts can be used to classify the severity of spinal deformity in lumbar/thoracolumbar AIS. …

Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification.

PURPOSE: To create a CT texture-based machine learning algorithm that distinguishes benign from potentially malignant cystic renal masses as … al software TexRAD (Feedback PLC, Cambridge, UK). Random forest (RF), logistic regression (LR), and support vector machine (SVM) machine learning algorithms were implemented to classify cystic renal masses into the two groups and tested with tenfold cross validations. RESULTS: Higher mean, standard deviation, mean of positive pixels, entropy, skewness were statisticall …

Dynamic monitoring of salicylic acid and vitamin B2 during the apple boiling process based on fluorescence spectroscopy and machine learning.

Thermal processing affects nutrient retention in fruits, yet quantitative in-process monitoring remains challenging. Excitation-emission mat … χ 2 tests; the synthetic minority over-sampling technique (SMOTE) was used for class balancing. Support vector machine (SVM) achieved 90.84-97.76% accuracy for boiling-stage classification ( κ = 0.9019-0.9761). Partial least squares (PLS) provided the best concentration prediction for both vitamin B 2 ( R 2 = 0 . 9734 , root mean square error (RMSE) = 0.0239) …

Incidence and Risk Factors of Lower Limb Deep Vein Thrombosis in Psychiatric Inpatients by Applying Machine Learning to Electronic Health Records: A Retrospective Cohort Study.

BACKGROUND: Psychiatric inpatients face an increased risk of deep vein thrombosis (DVT) due to their psychiatric conditions and pharmacological treatments. However, research focusing on this population remains limited. METHODS: This study analyzed 17,434 psychiatric inpatients at Huzhou Third Municipal Hospital, incorporating data on demographics, psychiatric diagnoses, physical illnesses, laboratory results, and medication use. Predictive models for DVT were developed using logistic regression, random forest, support vector machine (SVM), and XGBoost (Extreme Gradient Boosting). Feature impo…

Machine learning analysis for the association between breast feeding and metabolic syndrome in women.

This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010-2019. The dependent variable was metabolic syndrome. The 86 independen…

Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma.

Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with a high 5-year mortality rate. However, proteomic technologies have not yet been utilized to identify SNSCC-associated proteins, which could be used as biomarkers. In this study, we aimed to discover a biomarker to predict SNSCC patients using proteomic analysis integrated with machine learning models. Support vector machine (SVM), logistic regression (LR), random forest (RF), and gradient boost (GB) classifiers were developed to predict SNSCC based on proteomic profiles of SNSCC compared with nasal polyps (NP) as control. Seventeen…

Mobile app for targeted selective treatment of haemonchosis in sheep.

Livestock is an important part of many countries gross domestic product, and sanitary control impacts herd management costs. To contribute t … ning strategies were assessed, resulting in an accuracy of 83 % for a neural network and 87 % for a support vector machine (SVM). The SVM classifier was embedded into the app and made available for evaluation. This work is particularly interesting to small property owners from regions with difficult access or restrictions on obtaining continuous post-training technical …

Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer.

"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosi

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… (a) Support Vector Machine (SVM) is a popular classification method that excels with large datasets and yields more precise results. This can be accomplished by an even small electric locomotive using a “well-fitted optimisation” strategy in kernel mode. … Classification of malignant mesothelioma cancer using support vector machine. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET); 2018, March; Sukkur, Pakistan. pp. 1–5. …

A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.

The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE … brosis was moderate for pSWE (area under the curve [AUC]: 0.76) and 2-DSWE (0.84); the ML algorithm support vector machine (SVM) performed particularly well (pSWE: 0.96, 2-DSWE: 0.99). The results suggest that the multivendor ML-based algorithm SVM can binarily grade liver fibrosis using ultrasound elastography with excellent diagnostic performance, comparable to that o …

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features.

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) c…

Feasibility Assessment of Telehealth-Based Cancer Pain Management Through Machine Learning: A Prospective Clinical Study.

BACKGROUND: Although telehealth strategies can be effectively adopted to manage cancer pain, identifying the optimal care pathway for tailor … . Six models were tested: logistic regression, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), k-nearest neighbors (KNNs), and multilayer perceptron (MLP). Training and tuning used a 7-repeated 5-fold cross-validation approach. Performance was evaluated on a hold-out test set using F1-score, accuracy, and AUC-ROC. A sensitivity analysi …

Development and Validation of Machine-Learning-Based Prediction Models for Thyroid Diseases During Pregnancy.

BACKGROUND International guidelines recommend early screening based on targeted risk factors to identify thyroid disease during pregnancy. T … lgorithm. Eight models were developed: logistic regression, Bayesian approach, k-nearest neighbors, support vector machine, neural network, classification and regression tree, extreme gradient boosting, and random forest (RF). Model performance was evaluated using the receiver operating characteristic (ROC) curve, precision-recall curve (PRC), calibration curve, and dec …

Development of a risk prediction model for rheumatoid arthritis-associated interstitial lung disease based on interpretable machine learning.

OBJECTIVE: This study constructed a predictive model for interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA) and expl … ent boosting machine (LightGBM), logistic regression (LR), extreme gradient boosting (XGBoost), and support vector machine (SVM). The performance of these models was measured based on the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Shapley additive explanation (SHAP) algorithm was applied to conduct interpretabilit …

Use of Machine Learning Models to Predict Microaspiration Measured by Tracheal Pepsin A.

BACKGROUND: Enteral feeding intolerance, a common type of gastrointestinal dysfunction leading to underfeeding, is associated with increased … iated with enteral feeding were analyzed with 3 machine learning models-random forest, XGBoost, and support vector machines with recursive feature elimination-using 5-fold cross-validation tuning. RESULTS: Random forest for tracheal pepsin A was the best-performing model (area under the curve, 0.844 [95% CI, 0.792-0.897]; accuracy, 87.55%). The top 20 predictors of tra …

[Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].

OBJECTIVES: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablat … utcomes. Four machine learning algorithms, including k-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were coupled with 7 undersampling methods, namely Random Undersampling (RUS), Repeated Edited Nearest Neighbors (RENN), All k-Nearest Neighbors (AllKNN), Neighborhood Cleaning Rule-3 (NM), Condensed Nearest Ne …

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms.

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel appr … ant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performa …

Robust Detection of Major Depressive Disorder Using Empirical Mode Decomposition-Based EEG Signal Analysis.

Depression is universally known as a mood disorder, which is a serious and common disease that causes moral weakness and suicidal mortality. … w significant differences between MDD subjects and healthy subjects. Using the linear kernel of the support vector machine (SVM), the classification results achieve higher accuracy, after dimensionality reduction with the principal component analysis method. All performance measures including accuracy, precision, sensitivity, specificity, and negative predictive value r …

The abnormal audiovisual conflict in Parkinson's disease patients is manifested in perception rather than response.

Audiovisual conflict control is a crucial cognitive function in humans. However, it remains unclear whether and how abnormal audiovisual con … erference (with visual interference > auditory interference) at period of perception. Additionally, support vector machine (SVM) analyses revealed that models utilizing conflict at period of perception as a feature achieved higher predictive accuracy for participant classification compared to those employing conflict at period of response. These findings suggested that …