Digital Commons Network^™

Cross-Domain Disaggregation Of Electricity For Heating In All-Electric School Buildings – Learning From School Buildings With District Heating, Synne Krekling Lien, Ada Canaydin, Clayton Miller, Chun Fu, Hussain Kazmi, Jayaprakash Rajasekharan

Research Collection College of Integrative Studies

Electric heating is widespread in Norwegian buildings and significantly contributes to peak loads in the electricity grid. Non-residential buildings are typically heated either by district heating or a combination of electrical heating appliances. Despite its widespread use, most buildings lack sub-meters for electric heating. As a result, the true potential for energy efficiency and load flexibility from heating appliances in buildings remains unknown. Non-intrusive load monitoring and disaggregation techniques offer alternatives to sub-metering by using data-driven methods to extract electricity use for appliances from time-series data. However, little research has been conducted on disaggregating electrical heating loads from low-resolution data, …

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

Introducing Statistical And Machine Learning-Based Methods Of Enhancing The Resiliency And Security Of Electrical-Based Critical Infrastructure, Joshua Tyler

Masters Theses and Doctoral Dissertations

Since its introduction into society in the late 1800’s, electricity has become a critical component in how society has functioned. High-voltage electricity provides power for appliances that have become integral to daily living, such as lights, refrigeration, and Heating, Ventilation, And Cooling (HVAC). Low-voltage electricity is used to process and transmit information on and between computers. This information may pertain to medical, financial, and defense-related activities. Over the past hundred years, significant research has been performed to improve electrical-based technology’s capability, scale, resiliency, and security. This study focuses solely on resiliency and security, both of which require efficient data collection, …

A Deep Generative Model For Deciphering Cellular Dynamics And In Silico Drug Discovery In Complex Diseases, Yumin Zheng, Jonas C Schupp, Taylor Adams, Geremy Clair, Aurelien Justet, Farida Ahangari, Xiting Yan, Paul Hansen, Marianne Carlon, Emanuela Cortesi, Marie Vermant, Robin Vos, Laurens J De Sadeleer, Ivan O Rosas, Ricardo Pineda, John Sembrat, Melanie Königshoff, John E Mcdonough, Bart M Vanaudenaerde, Wim A Wuyts, Naftali Kaminski, Jun Ding

Faculty, Staff and Students Publications

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals …

A Molecular Renaissance In Alzheimer's Disease Research: The Rise Of Systems Biology And Spatial Omics, Lucia M. Perez-Navarro, Juan Lopez-Alvarenga

School of Medicine Publications

Recent advances in multi-omics and spatial proteomics are reshaping our understanding of Alzheimer's disease. Guo et al.1 applied integrative multi-omics to stratify mild cognitive impairment into biologically distinct subtypes with divergent progression trajectories: one metabolically impaired and slow-progressing, the other immune-activated and rapidly declining. Current techniques such as STC-DESI, LCM-MS, and machine learning enhance regional proteomic resolution, supporting biomarker discovery and spatially targeted interventions. This work exemplifies a broader shift toward precision medicine and a systems-level molecular framework in neurodegenerative disease research.

Interpretive Prediction Of Hyperuricemia And Gout Patients Via Machine Learning Analysis Of Human Gut Microbiome, Jia Wei Tang, Alfred Chin Yen Tay, Liang Wang

Research outputs 2022 to 2026

Hyperuricemia (HUA) and gout result from imbalances in uric acid metabolism and are closely associated with the gut microbiota. Advanced analytical methods facilitate the exploration of microbiota complexity. In this study, 16S rRNA sequencing data from stool samples of 233 patients were thoroughly collected. Machine learning (ML) and Shapley Additive exPlanations (SHAP) interpretability algorithms were applied to identify core taxa and predict the metabolic functions. The results revealed that the high-contribution core taxa identified by SHAP in each group, such as Oscillospiraceae_UCG-005 and Rhodococcus provided the basis for ML prediction. Among the five classification models, Random Forest (RF) achieved the …

Physics-Guided Strategies For Enhancing Neural Networks Trained With Limited Data, Jose Guadalupe Perez Zamora

Open Access Theses & Dissertations

Deep neural networks excel at a wide range of processing tasks across various disciplines. However, the quantity and quality of data significantly impact network performance. In specialized domains, high-quality datasets are often difficult to gather, interpret, and curate for effective learning. Few-shot learning techniques, including transfer learning, data augmentation, and meta-learning, have emerged to address these constraints.

We propose three physics-guided strategies for enhancing neural networks trained with limited data: (1) combining existing models like LSTMs with Physics-Informed Neural Networks through two-branch architectures that merge their outputs, (2) deriving custom physics-informed data augmentation algorithms to expand limited datasets, and (3) …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Advances In Artificial Intelligence For Glioblastoma Radiotherapy Planning And Treatment, Reid Master, Nesha Rubin, James Sampson, Kamlesh K Yadav, Shruti Pandita, Aria Sabbagh, Anika Krishnan, Patrick J Silva, Kenneth S Ramos, Vincent Gregoire, Nikos Paragios, Sunil Krishnan, Tej K Pandita

The Brown Foundation: Institute of Molecular Medicine

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy …

Fish-Spec: Fast Identification System For Handheld Spectroscopy And Species Classification, Mitchell Sueker, Nicholas Mackinnon, Gregory Bearman, Amanda Tabb, Diane Kim, Rosalee S. Hellberg, Alireza Akhbardeh, Hamid Reza Marateb, Jianwei Qin, Moon Kim, Fartash Vasefi, Hossein Kashani Zadeh

Food Science Faculty Articles and Research

Accurate fish species identification is critical to prevent mislabeling and fraud in the seafood industry. We present a handheld multi-mode point spectroscopy system that combines fluorescence (365 and 395 nm excitation) and reflectance measurements in the visible to near-infrared (∼350–900 nm) and short-wave infrared (∼900–1700 nm) regions for rapid, non-destructive classification of fish fillets. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states. Feature-level fusion across all four modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, …

Field Canals Improvement Projects Duration Prediction: A Comparative Analysis Of Machine Learning Models, Hania Ghouse, Ukaegbu Chinonso Ishmael, Edgar Dario Obando-Paredes, Hashem Shafik Shakir, Ali Al-Bayaty

Electrical and Computer Engineering Faculty Publications and Presentations

There are several essential elements in project construction management to be studied appropriately, and priority to these elements, such as cost and duration, is predominantly interesting to be investigated. In this research, the duration of field canal improvement projects (DFCIP) was predicted using two relatively new machine learning (ML) models - the Multivariate Adaptive Regression Spline (MARS) and Extreme Learning Machine (ELM). The targeted DFCIP was calculated using other dependent parameters, such as the length of the pipe, years of construction, the geographical zone of the network, the supplied area with water, and finally the actual cost of the field …

Archaeological Predictive Meta-Modeling In Pre-Columbian Mexico, Peter Stamm

Electronic Theses and Dissertations

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to …

Navigating Structure, Function, And Stability: Bridging Immunotherapy And Protein Engineering In The Era Of Machine Learning, Christopher J. Haynie

Theses and Dissertations

The immune system is a remarkably adaptive network, yet it often fails to function optimally due to evasion strategies of pathogens or mutated cells. Many of these evasion strategies involve alterations in protein expression and function. Protein design is an area of computational biology with potential applications in improving protein function and therapeutic protein engineering. This thesis explores methods to enhance immune function and protein design across multiple scales, from molecular engineering of antibody fragments and chimeric antigen receptors (CARs) to cellular immune responses, and computational approaches for protein structure optimization. We characterize Thymidine Kinase 1-specific single-chain antibodies, highlighting both …

Machine Learning Model For Detecting Masked Hypertension In Young Adults, Brendyn Miller, Samuel Coeyman, Annemarie Wentzel, Carina M.C. Mels, William J. Richardson

Chemical Engineering Faculty Publications and Presentations

Introduction Cardiovascular disease (CVD) remains the leading global cause of mortality, with hypertension (HT) being a significant contributor, responsible for 56% of CVD-related deaths. Masked hypertension (MHT), a condition where patients exhibit normotensive blood pressure (BP) in clinical settings but elevated BP in out-of-clinic measurements, poses an elevated risk for cardiovascular complications and often goes undiagnosed. Current diagnostic methods, such as ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM), have limitations in feasibility and accessibility. Methods This study aimed to address these challenges by leveraging machine learning (ML) models to predict MHT based on clinical data from a single …

Interpretable Machine Learning Frameworks For Detecting And Characterizing Adaptive Processes In Genomic Data, Md Ruhul Amin

Electronic Theses and Dissertations 2020 - Present

Detecting genomic regions influenced by adaptive processes is fundamental to understanding adaptation and its connection to modern human traits. Traditional summary-statistic and likelihood methods capture only simple evolutionary scenarios, while deep learning models such as convolutional neural networks, though powerful, often lose spatial information and lack interpretability. This dissertation introduces three complementary frameworks that overcome these limitations by integrating interpretable machine learning with spatially structured genomic data. The first framework, T-REx employs tensor decomposition to extract informative features from haplotype alignment images, enabling accurate classification of selective sweeps with classical learning models. The second framework, α-DAWG applies wavelet and curvelet …

A Novel Hybrid Intrusion Detection Model: A New Metaheuristic Approach For Feature Selection Based On Ai Techniques For Cyber Threat Detection, Maryam Mahdi Alhusseini, Alireza Rouhi

Iraqi Journal for Computer Science and Mathematics

The rapid increase in internet usage, digital transformation, and the rise of interconnected devices have greatly expanded the attack surface, introducing new and evolving cybersecurity challenges. Conventional security solutions frequently have difficulty adjusting to complex threats and the vast dimensionality of network traffic data, particularly in the case of imbalanced datasets. To tackle these challenges, this research introduces a Hybrid Intrusion Detection System (HyIDS-EVO) that combines the Energy Valley Optimizer (EVO) for feature selection and dimensionality reduction with machine learning classifiers, which include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). The system’s effectiveness …

Integrated Optimization And Data-Driven Modeling For Seawater Intrusion Mitigation And Prediction, Assaad Hassan Kassem

Thesis/ Dissertation Defenses

Seawater intrusion (SWI) threatens the reliability of coastal groundwater especially in hyper-arid settings, where climatic stress and pumping accelerate salinization. This dissertation advances two complementary approaches to managing SWI: Part A optimizes mitigation measures, hydraulic (pumping/injection) and physical barriers (cutoff walls, subsurface dams) on benchmark models; Part B predicts SWI in the hyper-arid Fujairah (UAE) coastal aquifer using total dissolved solids (TDS) as a proxy, through machine learning-based models, spatially and dynamically. A bibliometric synthesis first maps the evolution of SWI models and mitigation strategies, identifying gaps that motivate the subsequent methodological developments. Part A employs the classical Henry problem …

Using Machine Learning To Predict Maintenance Intentions Of Septic System Owners, Audrey Mccrary, Luna Yang, Allen Berthold

Journal of Contemporary Water Research and Education

The scope of literature related to septic system maintenance from an educational and behavioral perspective, rather than a quantitative water quality approach, is limited. The water resources management community needs improved insight into the human factors related to septic system maintenance decisions and identification of effective intervention strategies. Using a modified Theory of Planned Behavior (TPB; Ajzen 1991) framework, the purpose of this study was to identify factors that predict homeowners’ intention to have their systems inspected and pumped within the next three years. As part of a small-scale educational program, a survey was sent to 1,374 homeowners with septic …

Developing A High-Throughput Machine Learning Screening Platform For Genetic And Pharmacological Modulators On Caenorhabditis Elegans Seizure Behavior, Paul A. Scarpinato

Electronic Theses and Dissertations 2020 - Present

Epilepsy is a chronic neurological disorder characterized by recurrent seizures that affect over 50 million people worldwide, including more than 3.5 million in the United States. Seizures arise from a disrupted balance between excitation and inhibition. Although antiepileptic drugs (AEDs) remain the primary treatment to restore the excitation and inhibition balance, approximately 30% of patients experience drug-resistant epilepsy, highlighting the need for additional antiseizure medications. The Caenorhabditis elegans electroshock assay was developed in relative alignment with the rodent maximal electroshock seizure (MES) test, the gold standard for preclinical AED screening, provides a cost-effective platform for prescreening candidate AEDs.

This work …

Hybrid Machine Learning Technique For Cybersecurity Event Detection, Ashwag Albakri

University of Bisha Journal for Basic and Applied Sciences

The use of machine learning algorithms has become essential in the cybersecurity industry. The increasing frequency of government intrusions and malicious actions has made the necessity for strong defenses and protective measures vital. Designing an automated and efficient digital threat detection mechanism is one of the most difficult tasks in network security. This study proposes a cyber-event detection model that effectively forecast cyber-occurrences to resolve this issue. As a result, a hybrid machine learning model using support vector machines (SVM) with random forest (RF) is proposed. The proposed algorithm is termed HS-RF. By identifying and thwarting harmful attacks, the hybrid …

Advancing Road Safety Through Data Integration, Location Accuracy, And Risk-Based Route Optimization, Lars Skaug

Electronic Theses and Dissertations 2020 - Present

This dissertation advances transportation safety through three interconnected contributions that address fundamental challenges in crash data analysis and application. First, we present a comprehensive survey of road crash analysis methodologies, tracing the evolution from traditional statistical approaches to modern machine learning techniques while identifying critical gaps between research sophistication and practical implementation. This survey reveals that while methodological advances have been substantial, ongoing data quality issues, particularly in location accuracy and completeness, limit the effectiveness of even the most sophisticated analytical approaches.

Building on these findings, we develop a novel system to validate and correct crash location data using multi-modal …

A Multiomics Recovery Factor Predicts Long Covid In The Impacc Study, Gisela Gabernet, Jessica Maciuch, Jeremy P Gygi, John F Moore, Annmarie Hoch, Caitlin Syphurs, Tianyi Chu, Naresh Doni Jayavelu, David B Corry, Farrah Kheradmand, Lindsey R Baden, Rafick-Pierre Sekaly, Grace A Mccomsey, Elias K Haddad, Charles B Cairns, Nadine Rouphael, Ana Fernandez-Sesma, Viviana Simon, Jordan P Metcalf, Nelson I Agudelo Higuita, Catherine L Hough, William B Messer, Mark M Davis, Kari C Nadeau, Bali Pulendran, Monica Kraft, Chris Bime, Elaine F Reed, Joanna Schaenman, David J Erle, Carolyn S Calfee, Mark A Atkinson, Scott C Brakenridge, Esther Melamed, Albert C Shaw, David A Hafler, Alison D Augustine, Patrice M Becker, Al Ozonoff, Steven E Bosinger, Walter Eckalbar, Holden T Maecker, Seunghee Kim-Schulze, Hanno Steen, Florian Krammer, Kerstin Westendorf, Bjoern Peters, Slim Fourati, Matthew C Altman, Ofer Levy, Kinga K Smolen, Ruth R Montgomery, Joann Diray-Arce, Steven H Kleinstein, Leying Guan, Lauren Ir Ehrlich

Faculty, Staff and Students Publications

Background: Following SARS-CoV-2 infection, approximately 10%–35% of patients with COVID-19 experience long COVID (LC), in which debilitating symptoms persist for at least 3 months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities.

Methods: We utilized machine learning methods on biologic analytes provided over 12 months after hospital discharge from more than 500 patients with COVID-19 in the IMPACC cohort to identify a multiomics “recovery factor,” trained on patient-reported physical function survey scores. Immune profiling data included PBMC transcriptomics, serum O-link and plasma proteomics, plasma metabolomics, and blood mass cytometry by time of flight (CyTOF) protein levels. Recovery factor …

Advancing Applications Of Deep Learning On Network Traffic Analysis And Fingerprinting, Nate Mathews

Theses

The growing reliance on encrypted communication networks and privacy-preserving technologies, such as Tor, has intensified the demand for advanced defenses against traffic analysis attacks. Although encryption conceals content, the exposure of metadata—such as timing, packet size, and traffic volume—remains a significant vulnerability, allowing adversaries to infer private user behavior and visited websites. This dissertation addresses the limitations of existing analysis by advancing the application of deep learning techniques across multiple domains of traffic analysis and fingerprinting. The first major contribution is a comprehensive evaluation of current website fingerprinting defenses, revealing critical weaknesses against sophisticated modern attacks. Building on this foundation, …

Towards Automated And Explainable Insider Threat Response In Electronic Health Records: A Role-Aware Machine Learning Framework, Luca Lippi Ornstil

Master's Theses

Healthcare remains a prime target for cyberattacks, with insider misuse and credential compromise posing major risks to Electronic Health Records (EHRs). This thesis introduces a role-aware, explainable anomaly detection and response framework integrated with OpenEMR to address post-authentication threats. Four models—Local Outlier Factor (LOF), Isolation Forest, Autoencoder, and Graph Neural Network (GNN)—detect behavioral deviations across temporal, device, and role-based features, with LOF serving as the primary runtime detector. A configurable policy engine maps anomaly severity to proportional actions, from email alerts to read-only restrictions or account suspension, all reversible and auditable. Evaluation on real EHR logs shows the system’s operational …

The Role Of Fungal Proteases In Bioactive Peptide Production And Analytical Approaches For Tracking Their Activity, Tanvir Ahmed, Angéla Juhász, Utpal Bose, Netsane Shiferaw Terefe, Michelle L. Colgrave

Research outputs 2022 to 2026

Fungal proteases catalyse site-specific peptide bond cleavage in complex protein matrices, enabling bioactive peptide (BP) release. Accurate monitoring is vital for optimising hydrolysis efficiency, minimising nonspecific proteolysis, ensuring consistent yields, and preserving peptide biofunctionality. While analytical and computational tools are well-established for non-fungal systems, their adaptation to fungal proteases remains exploratory, with no comprehensive evaluation addressing the unique challenges in this field. This review synthesises current knowledge, identifies methodological gaps, and outlines future directions for developing fungi-specific analytical pipelines and computational frameworks for BP discovery. It explores fungal protease families, their roles in intracellular peptide processing, and fungal survival strategies. …

Possibilities Of Digitizing And Applying Artificial Intelligence To National Occupational Classification (Noc-2025) In Uzbekistan, Shohrux Nurali O‘G‘Li Narzullayev

Chemical Technology, Control and Management

This article examines the process of digitizing National Occupational Classification (NOC-2025) in Uzbekistan, developed on the basis of the International Standard Classification of Occupations (ISCO-08), and the possibilities of applying artificial intelligence technologies to it. Although this classification exists today in a national form, and its digitization and the introduction of artificial intelligence elements to it based on modern technologies remain a pressing issue. In order to digitize the classification, international systems such as the International Standard Classification of Occupations (ISCO-08, ILO), European Skills, Competences, Qualifications and Occupations (ESCO), Occupational Information Network (O*NET, USA) and National Occupational Classification (NOC, Canada) …

Framework For Next Generation Security Operation Center Powered By Artificial Intelligence, Mohamad Khayat

Thesis/ Dissertation Defenses

This dissertation presents a comprehensive framework for the evolution of Security Operation Centers (SOCs) through the integration of advanced artificial intelligence (AI), blockchain, and optimization techniques. Motivated by the increasing complexity of cyber threats and the limitations of traditional reactive SOC strategies, this work begins with a systematic literature review that identifies critical gaps in current SOC operations. Based on these insights, a reference architecture is proposed to guide the integration of intelligent components into SOC environments. To address the challenge of secure and trustworthy information sharing, a blockchain-based threat intelligence platform is developed, leveraging Byzantine Fault Tolerance and Zero-Knowledge …

Computational Nanoscale Investigations Of Structure, Dynamics, And Reactivity At Solid–Liquid Interfaces, Golam Azom

LSU Doctoral Dissertations

Interfaces where two or more condensed phases meet govern phenomena central to catalysis, electrochemical energy conversion, separations, and environmental remediation. Their structural heterogeneity and dynamic nature make them challenging to probe experimentally, but computer simulations provide molecular-level insights into processes that remain inaccessible to experiments. This dissertation employs ab initio molecular dynamics (AIMD), classical molecular dynamics, and machine-learned interatomic potentials to investigate the structure, dynamics, and proton transport at graphene oxide (GO)–liquid interfaces.

The first project explores aqueous GO interfaces across a spectrum of oxidation states that extends from highly oxidized sheets to fully reduced graphene. Deep neural network force …

Green Carbon Dots In The Era Of Ai: Sustainable Synthesis, Intelligent Drug Delivery, Advanced Diagnostics, And Bioimaging, Emi̇ne Sezer, Fulden Ulucan Karnak, Si̇nan Akgöl

Turkish Journal of Biology

Background/aim: Green carbon dots (GCDs) are a rapidly developing class of nanomaterials that are revolutionizing various scientific disciplines due to their unique optical properties, low toxicity, and sustainable synthesis. This review offers a comprehensive roadmap for the field, emphasizing the synergy between GCDs and artificial intelligence (AI).

Materials and methods: We begin by detailing the sustainable synthesis of GCDs, highlighting green chemistry principles and the transformative role of AI in optimizing their production. Subsequently, we explore the critical characterization of GCDs, including their structural, optical, and biocompatibility assessment. The core of this study explores the diverse biomedical applications of GCDs, …

A Systematic Review Of Machine Learning In Heart Disease Prediction, Tathagat Banerjee, İshak Paçal

Turkish Journal of Biology

Background/aim: Cardiovascular diseases (CVDs) are a leading cause of global mortality, prompting the need for advanced predictive tools. While machine learning (ML) offers a powerful solution, there are significant challenges to clinical translation. This systematic review synthesizes the current state of ML in heart disease prediction, evaluating algorithmic performance, data utilization, and key translational challenges.

Materials and methods: Following PRISMA guidelines, a systematic search of literature published up to 2025 was conducted. From an initial pool of over 2500 records, a rigorous screening process yielded 65 studies for in-depth qualitative synthesis.

Results: Analysis showed that ensemble learning models dominate prediction …