Comparison of ML and NLP algorithms for classifying the brain MRI reports. by Chulho Kim (622686)

Variable Selection and Estimation for Misclassified Binary Responses and Multivariate Error-Prone Predictors by Li-Pang Chen (9747423)

“…<p>In statistical analysis or supervised learning, classification has been an attractive topic. …”

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke by Chulho Kim (622686)

“…</p><p>Conclusions</p><p>Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. …”

Parameters of the experiments. by Enrico Zardini (17382523)

“…A well-known locality technique is the <i>k</i>-nearest neighbors (<i>k</i>-NN) algorithm, of which several quantum variants have been proposed; nevertheless, they have not been employed yet as a preliminary step of other QML models. …”

Quantum pipeline workflow overview. by Enrico Zardini (17382523)

“…A well-known locality technique is the <i>k</i>-nearest neighbors (<i>k</i>-NN) algorithm, of which several quantum variants have been proposed; nevertheless, they have not been employed yet as a preliminary step of other QML models. …”

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. by Meghana Venkata Palukuri (11890672)

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study by Victoria C. Merritt (8581929)

“…Cross-validation was used to train and evaluate the proposed algorithm, ‘TBI-PheCAP.’ TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (<i>n</i> = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. …”

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports by Olivier Q. Groot (9370461)

“…The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases.…”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…By training models on labeled genomic data (e.g., the presence or absence of resistance genes, SNP profiles, or MLST types), these classifiers help identify patterns and make accurate predictions.</p><h4><b>10 Supervised machine learning classifiers for </b><b><i>E.coli</i></b><b> genome analysis:</b></h4><ol><li><b>Logistic regression (LR): </b> A simple yet effective statistical model for binary classification, such as predicting antibiotic resistance or susceptibility in <i>E. coli</i>.…”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…<br><br>Methods<br><br>This work is a quantitative and experimental study of supervised classification. The sample was extracted from the "Mushroom" dataset from the UCI repository, containing 8,124 instances. …”

Table_1_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional s... by Niruwan Turnbull (11506910)

“…Objective<p>To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.</p>Methods<p>The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ² and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.…”

Table_2_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional s... by Niruwan Turnbull (11506910)

“…Objective<p>To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.</p>Methods<p>The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ² and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.…”

Data_Sheet_1_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectio... by Niruwan Turnbull (11506910)

“…Objective<p>To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.</p>Methods<p>The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ² and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.…”

The Value of Dynamic Grip Force Modulation as a Potential Biomarkerfor Hand Function Recovery Following Stroke by Kirstin-Friederike Heise (7518953)

“…</p><p dir="ltr">We used a supervised machine learning algorithm (support vector machine, SVM, with k-fold cross-validation) for binary classification of groups (stroke versus control group), task conditions (uni- versus bimanual), and to quantify the active range of motion evaluated with upper extremity Fugl-Meyer Assessment (UEFMA) within the stroke group alone.…”

Table_1_Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.docx by Markus Huber (317962)

“…</p>Objective<p>To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. …”

Integrating terahertz time-domain spectroscopy with XGBoost for rapid and interpretable species-level wood identification of <i>Pterocarpus</i> by Min Yu (120607)

“…The results showed that the XGBoost model performed best, achieving 100% accuracy in binary classification (<i>P</i>. <i>santalinus</i> and <i>P</i>. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”