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Narrative review of the development of an ischaemic heart disease prognostic scoring tool (i-IHD score) among patients with type 2 diabetes mellitus in Malaysia.

INTRODUCTION: Ischaemic heart disease (IHD) remains a major cause of mortality among individuals with type 2 diabetes mellitus (T2DM) in Mal … sion and Cox models) with AI-calibrated models such as extreme gradient boosting, random forest and support vector machines were reviewed. RESULTS: Eleven studies met the inclusion criteria; four used conventional statistical methods, and seven applied AI or machine learning algorithms. The reported discrimination (area under the curve=0.66-0.94) varied widely. Convent …

The role of artificial intelligence in advancing scoliosis care: a rapid review of current evidence and future opportunities.

BACKGROUND: Adolescent idiopathic scoliosis (AIS), is a complex three-dimensional deformity of the spine that affects a significant percenta … used AI algorithms with convolutional neural networks, artificial neural networks, decision trees, support vector machines, and hybrid models. The main applications in AIS were automatic Cobb angle measurement with high accuracy (< 3° in some models), curve type classification, prediction of curve progression and patient education and clinical decision support using la …

Machine Learning for Differentiating Essential Tremor: A Scoping Review.

BackgroundEssential tremor (ET) is the most common movement disorder, affecting ~6% of adults over 65 [1]. Differentiating ET from other tremors remains clinically challenging due to overlapping features and variable presentation. Artificial intelligence (AI), particularly machine learning (ML), has emerged a potential tool to support neurologists by enhancing pattern recognition and complementing traditional assessments in complex cases. This is the first scoping review examining ML's potential role in distinguishing essential tremor from other tremor types.MethodsA systematic, scoping search

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… Vector Machine. … The most common approach was support vector machines (SVM), used in 18 studies, followed by k-nearest neighbors (KNN) in nine studies and convolutional neural networks (CNN) in eight studies. …

Machine Learning Model for Predicting Walking Ability in Lower Limb Amputees.

Background/Objectives: The number of individuals with lower limb loss (LLL) is rising. Therefore, identifying the walking potential in indiv … ctional ability, a Timed Up and Go Test (TUG), and a Two-Minute Walking Test (TMWT) were performed. Support vector machines (SVM) were used to develop the prediction model. Results: Three decision trees were created, one for each output, as follows: K-level, TUG, and TMWT. For all three outputs, there were eight significant predictors (balance, body mass index, age, Bec …

Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.

The study presents a novel technique for lung auscultation based on graph theory, emphasizing the potential of graph parameters in distingui … parameters as input attributes, the supervised machine learning techniques- discriminant analyses, support vector machines, k-nearest neighbors, and neural network pattern recognition (PRNN)- classify the signals with more than 90% accuracy, with PRNN having 25 neurons in the hidden layer achieving the highest (98.82%).

Evaluating Voice-Assistant Commands for Dementia Detection.

Early detection of cognitive decline involved in Alzheimer's Disease and Related Dementias (ADRD) in older adults living alone is essential … 's use of the VAS. We then built machine-learning models including 1-layer/2-layer neural networks, support vector machines, decision tree, and random forest, for classification and comparison with standard cognitive assessment scores, e.g., Montreal Cognitive Assessment (MoCA). Our classification models using fusion features achieved an accuracy of 68%, and our regress …

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.

OBJECTIVE: Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose … ning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most …

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

PURPOSE: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address. METHODS: We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decisio

A spline-based tool to assess and visualize the calibration of multiclass risk predictions.

When validating risk models (or probabilistic classifiers), calibration is often overlooked. Calibration refers to the reliability of the pr … and test sets. We developed and tested models using the following algorithms: logistic regression, support vector machines, k nearest neighbors, random forest, naive Bayes and nearest shrunken centroids. Multiclass calibration plots are interesting as an approach to visualizing the reliability of predicted risks. The ECI is a convenient tool for comparing models, but i …

Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion.

PURPOSE: This study aims to develop a assessment system for evaluating shoulder joint muscle strength in patients with varying degrees of up … ere analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. Model performance was evaluated using Root Mean Squared Error (RMSE), R-Square (R 2), Mean Absolute Error (MAE), and Mean Bias Error (MBE), to identify the most accurate regression predic …

Direct Reconstruction of High-Fidelity Electrocardiogram Signals From Vector-Based PDF Files With Integrated Deep Learning for Multiparameter Estimation: Retrospective Methodological Study.

BACKGROUND: Electrocardiograms (ECGs) are commonly stored in PDF, particularly as vector-based files generated by ECG management systems. Previous studies have demonstrated that ECG signals can be extracted through PDF-to-Scalable Vector Graphics (SVG) conversion, highlighting the potential to reconstruct waveform signals from vector graphics. These reconstructed signals further enable the derivation and prediction of clinically relevant ECG parameters. OBJECTIVE: This study aimed to develop an integrated framework for direct reconstruction of high-fidelity ECG signals from vector-based PDF f

Automated detection of Chagas disease from ECG signals using wavelet scattering transform and RUSBoost classifier.

Objective.Early diagnosis of Chagas disease plays a vital role in enabling timely treatment and reducing the likelihood of underlying severe cardiovascular complications. Electrocardiogram (ECG) signals contain vital information that reflects cardiac disease progression, motivating the use of advanced signal processing and machine intelligence for accurate diagnosis of Chagas disease. This work presents an automated system for Chagas disease detection using conventional 12-lead ECG recordings.Approach.The method begins with preprocessing by standardizing the ECG sampling frequency and detectin

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early dia … es. Furthermore, combining these refined spectral features with machine learning classifiers (e.g., support vector machines) achieves an overall accuracy of 97.50 % and a sensitivity of 100.00 %. This work not only confirms the value of NIRS in detecting protein-level molecular information in blood but also presents a promising, efficient spectroscopic strategy for earl …

Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.

BACKGROUND: Inflammatory bowel disease (IBD) is a complex chronic disease of the gastrointestinal tract. This systematic review aimed at hig … 2021 and 2023. Random forest (RF) was identified as the most commonly used (n=10, 32%), followed by support vector machines (n=9, 29%), and the majority of the studies were focused on model evaluation metrics of ML. CONCLUSIONS: Our findings indicate that ML holds the potential to enhance diagnostic accuracy in distinguishing between ulcerative colitis and CD, particul …

Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence.

This comprehensive review examines the integration of Quantitative Electroencephalography (qEEG) and Artificial Intelligence (AI) in the det … eaching 93.18% accuracy and 97.92% Area Under Curve (AUC). Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) also showed promising results, with some models achieving up to 100% sensitivity in specific classifications. The integration of multiple data types and advanced feature extraction methods significantly improved diagnostic precision. Geograp …

Artificial intelligence in computational modeling of thrombosis: Bridging mechanistic insights and clinical translation.

Thrombosis presents significant healthcare challenges due to its complex nature. Recent advancements in data-driven mathematical and computa … hreshold prediction, shear rate prediction, and multiscale modeling. Artificial neural networks and support vector machines were the most commonly used ML models. The hybrid approach combining ML and computational models is still nascent but shows significant promise for advancing thrombosis research. These models offer valuable insights for improving thrombosis diagnos …

Comprehensive analysis of senescence-related genes identifies prognostic clusters with distinct characteristics in glioma.

Cellular senescence, defined as a state of permanent arrest in cell growth, is regarded as a crucial tumor suppression mechanism. However, a … EA (single-sample Gene Set Enrichment Analysis), and machine learning algorithms (lasso regression, support vector machines, random forests), were employed for analysis. We found that CEBPB and LMNA are associated with poor prognosis in gliomas and may mediate immunosuppression and tumor proliferation. Drug prediction indicated that dasatinib is a potential therapeutic …

Study on coal and gas outburst prediction technology based on multi-model fusion.

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has opened up novel avenues for predicting coal … ch constituent learner. Specifically, a Stacking-based ensemble model is constructed, incorporating Support Vector Machines (SVM), Random Forests (RF), and k-Nearest Neighbors (KNN) as base learners. An attention mechanism is then employed to adaptively weight the outputs of these base learners, thereby harnessing their complementary strengths. The meta-learner, primari …

Machine Learning Approaches for Integrating Clinical and Radiographic Data in the Early Detection of Osteonecrosis of the Jaw : Machine Learning Approaches for Integrating Clinical Data in Osteonecrosis of the Jaw.

Osteonecrosis of the jaw (ONJ) is a severe bone condition characterized by the progressive destruction of the jawbone, often associated with … al and radiographic data. ML algorithms, including supervised learning methods like random forests, support vector machines, and deep learning models such as convolutional neural networks, are particularly suited for analyzing complex datasets and identifying patterns that are undetectable by traditional methods. These models can enhance the sensitivity and specificity …

Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning.

Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease mostly affecting the exocrine glands. This debilitating condition … ee machine learning algorithms, including artificial neural networks (ANN), random forest (RF), and support vector machines (SVM), were used to build a pSS diagnostic model. The performance of the model was measured using its area under the receiver operating characteristic curve. Immune cell infiltration was investigated using the CIBERSORT algorithm. A total of 96 DEG …