Background/Objectives: This study aims to build an interpretable and accurate predictive model for myocardial infarction (MI) using Explainable Boosting Machines (EBM), a state-of-the-art Explainable Artificial Intelligence (XAI) technique. The objec...
Examining the factors contributing to work zone crashes and implementing measures to reduce their occurrence can significantly improve road safety. In this research, we utilized the explainable boosting machine (EBM), a modern glass-box machine learn...
Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved...
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, often diagnosed at late stages due to the limited sensitivity of current screening tools. This study explores whether blood-based lipidomic profiling...
There is growing tension between high-performance machine-learning (ML) models and explainability within the scientific community. In arsenic modelling, understanding why ML models make certain predictions, for instance, “high arsenic” in...
Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30...
Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest neighbors (kNN), random forests (RF), support vector machines (SVM), and boosted decision trees (DTs), may offer stronger predictive performance than more traditional,...
Background and Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA pat...
Accounting for geotechnical property variability is crucial in seismic site response analysis. Traditionally, the influence of each geotechnical property on response parameters is assessed independently. However, this approach limits our understandin...
Explainable artificial intelligence (XAI) is essential for fostering trust, transparency, and accountability in machine learning systems, particularly when applied in high-stakes domains. This paper introduces a novel XAI system designed for classifi...
Background: Thalassemia has been recognized as a critical public health issue in Bangladesh, especially among pregnant women, due to its hereditary nature and the lack of early screening infrastructure. Early identification of at-risk individuals is...
Acute psychological stress (APS) is a complex and multifactorial phenomenon that affects metabolism, necessitating real-time detection and interventions to mitigate its effects on glycemia in people with type 1 diabetes. This study investigates the d...
Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new ins...
This study assessed the applicability of visible–near-infrared (vis-NIR) spectroscopy to predicting the water retention characteristics of forest soils in Japan, which vary widely owing to the presence of volcanic ash. Soil samples were collect...
Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only...
The adoption of low-crested and submerged structures (LCS) reduces the wave behind a structure, depending on the changes in the freeboard, and induces stable waves in the offshore. We aimed to estimate the wave transmission coefficient behind LCS str...
Background/Objectives: Vitamin B12 deficiency is a prevalent yet frequently underdiagnosed condition, largely due to the limited diagnostic accuracy of serum total B12 and the restricted availability of confirmatory biomarkers such as holotranscobala...
Background: Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider...
In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation...
The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogen...
With the rural-to-urban population migration under the new era of rapid urbanization, China has experienced dramatic rural land change, especially the change in cultivated land and rural residential land, resulting in the serious uncoordinated human&...
The bandgap is a critical factor influencing the energy density of batteries and a key physical quantity that determines the semiconducting behavior of materials. To further improve the prediction accuracy of the bandgap in silicon oxide lithium-ion...
Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered va...
Unconfined compressive strength (UCS) is a critical property for assessing the engineering performances of sustainable materials, such as cement–fly ash mortar (CFAM), in the design of construction engineering projects. The experimental determi...
Mitigation of the heat island effect is critical due to the frequency of extremely hot weather. Urban street greening can achieve this mitigation and improve the quality of urban spaces and people’s welfare. However, a clear definition of stree...
Fishery resources of tuna serve as a vital source of global protein. This study investigates the key environmental drivers influencing the spatial distribution of yellowfin tuna (Thunnus albacares) in the western tropical Pacific Ocean. A comprehensi...
Predicting road crash severity remains a major challenge in transportation safety research, requiring models that combine predictive accuracy, interpretability, and computational efficiency. This study introduces a Multi-Risk Index based on Copula In...
In this paper, we propose using explainable artificial intelligence (XAI) techniques to predict and interpret the effects of local festival components on tourist satisfaction. We use data-driven analytics, including prediction, interpretation, and ut...
Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the...
The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due...
Banks’ credit scoring models are required by financial authorities to be explainable. This paper proposes an explainable artificial intelligence (XAI) model for predicting credit default on a unique dataset of unsecured consumer loans provided...
As the backbone of modern society and industry, the need for a more efficient and sustainable electrical grid is crucial for proper energy management. Governments have recognized this need and have included energy management as a key component of the...
In this paper, we explore the computational capabilities of advanced modeling tools to reveal the factors that shape the observed benzene levels and behavior under different environmental conditions. The research was based on two-year hourly data con...
This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of dec...
Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds, among which 16 are identified as priority pollutants, due to their adverse health effects, frequency of occurrence, and potential for human exposure. This study is...
The Eastern Cooperative Oncology Group (ECOG) performance status is a widely used method for evaluating the functional abilities of cancer patients and predicting their prognosis. It is essential for healthcare providers to frequently assess the ECOG...
Volatile Organic Compounds (VOCs) are important contributors to indoor and occupational air pollution, such as environments involving the extensive use of paints and solvents. The routine measurement of VOCs is often limited by resource constraints,...
Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured par...
Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious public health problem globally. Substantial efforts have been made to apply machine learning in detecting the virus. However, the application of mode...
Background: This study aimed to create and evaluate explainable machine learning models for forecasting postoperative stone-free status following percutaneous nephrolithotomy (PNL) utilizing a substantial clinical cohort. Methods: This retrospective...
Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The ad...
Depression is a common mental health disorder and a leading contributor to mortality and morbidity. Despite several advancements, the current screening methods have limitations in enabling the robust and automated detection of depression, thereby hin...
Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high meltin...
With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and u...
The aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by...
The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through...
Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features d...
The monkeypox virus poses a novel public health risk that might quickly escalate into a worldwide epidemic. Machine learning (ML) has recently shown much promise in diagnosing diseases like cancer, finding tumor cells, and finding COVID-19 patients....
The current study examines the application of advanced machine learning (ML) techniques for forecasting credit risk in Islamic (participation) and traditional banks in the United Kingdom in 2010–2023. Leveraging an equally weighted panel datase...
Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this...
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