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Статті в журналах з теми "Risk assessment algorithms"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Arora, Kavleen, Olga Kostopoulou, and Bence Palfi. "Cancer risk algorithms in primary care: can they impact risk estimates and referral decisions?" British Journal of General Practice 73, suppl 1 (2023): bjgp23X733773. http://dx.doi.org/10.3399/bjgp23x733773.

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BackgroundCancer risk algorithms were introduced to clinical practice in the last decade, but they remain underused.AimIn two randomised controlled experiments, we tested the impact of an unnamed cancer risk algorithm (QCancer) on GPs’ risk assessment and 2-week-wait referral decisions. We also tested the impact of algorithm information, ‘social proof’, and a visual explanation.MethodWe presented two different samples of GPs (totaln=372) with vignettes depicting patients with possible colorectal (Experiment 1) or upper GI (Experiment 2) cancers and measured their risk estimates and inclination

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2

Hussain, S. Monira, Brian Oldenburg, Yuanyuan Wang, Sophia Zoungas, and Andrew M. Tonkin. "Assessment of Cardiovascular Disease Risk in South Asian Populations." International Journal of Vascular Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/786801.

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Although South Asian populations have high cardiovascular disease (CVD) burden in the world, their patterns of individual CVD risk factors have not been fully studied. None of the available algorithms/scores to assess CVD risk have originated from these populations. To explore the relevance of CVD risk scores for these populations, literature search and qualitative synthesis of available evidence were performed. South Asians usually have higher levels of both “classical” and nontraditional CVD risk factors and experience these at a younger age. There are marked variations in risk profiles betw

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3

MARINAKIS, YANNIS, MAGDALENE MARINAKI, and CONSTANTIN ZOPOUNIDIS. "APPLICATION OF ANT COLONY OPTIMIZATION TO CREDIT RISK ASSESSMENT." New Mathematics and Natural Computation 04, no. 01 (2008): 107–22. http://dx.doi.org/10.1142/s1793005708000957.

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This paper presents a novel approach to solve feature subset selection problems using an Ant Colony Optimization (ACO) algorithm. ACO is one of the important naturally inspired intelligent techniques. It is based on the foraging behavior of real ants in nature. The proposed ACO is combined with a number of nearest neighbor classifiers. The resulting ACO algorithm is applied to classify credit risk using data belonging to 1,411 firms obtained from a leading Greek commercial bank. The objective is to classify subject firms into several groups representing different levels of credit risk. The res

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4

Attigeri, Girija V., M. M. Manohara Pai, and Radhika M. Pai. "Credit Risk Assessment Using Machine Learning Algorithms." Advanced Science Letters 23, no. 4 (2017): 3649–53. http://dx.doi.org/10.1166/asl.2017.9018.

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5

Bukatina, T. M. "Risk Assessment Algorithms in Pharmacovigilance: A Review." Safety and Risk of Pharmacotherapy 13, no. 2 (2025): 138–48. https://doi.org/10.30895/2312-7821-2025-13-2-138-148.

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INTRODUCTION. In the Russian Federation, risk-based approaches/methods to assess the safety of medicinal products have been used since 2016, but existing models based on them are few and applicable mainly to healthcare organizations. This underscores the need to systematise risk assessment procedures for medicinal products within pharmacovigilance frameworks by pharmacovigilance specialists using a risk-based approach in the risk management system.AIM. This study aimed to analise of the key tools of the risk-based approach and optimise their application in medicinal product risk management sys

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6

Dong, Yumin, Ziyang Li, and Changzuo Xie. "Enhancing Forest Fire Risk Assessment: An Ontology-Based Approach with Improved Continuous Apriori Algorithm." Forests 15, no. 6 (2024): 967. http://dx.doi.org/10.3390/f15060967.

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Forest fires are sudden and difficult to extinguish, so early risk assessment is crucial. However, there are currently a lack of suitable knowledge-mining algorithms for forest fire risk assessment. This article proposes an improved continuous Apriori algorithm to mining forest fire rules by introducing prior knowledge to classify input data and enhance its ability to process continuous data. Meanwhile, it constructs an ontology to provide a standardized expression platform for forest fire risk assessment. The improved continuous Apriori algorithm cooperates with ontology and applies the minin

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7

Haescher, Marian, Wencke Chodan, Florian Höpfner, et al. "Automated fall risk assessment of elderly using wearable devices." Journal of Rehabilitation and Assistive Technologies Engineering 7 (January 2020): 205566832094620. http://dx.doi.org/10.1177/2055668320946209.

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Introduction Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices. Methods In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automated algorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observation

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8

Etra, Aaron, Stephanie Gergoudis, George Morales, et al. "Assessment of systemic and gastrointestinal tissue damage biomarkers for GVHD risk stratification." Blood Advances 6, no. 12 (2022): 3707–15. http://dx.doi.org/10.1182/bloodadvances.2022007296.

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Abstract We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378)

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9

Cervone, G., P. Franzese, Y. Ezber, and Z. Boybeyi. "Risk assessment of atmospheric emissions using machine learning." Natural Hazards and Earth System Sciences 8, no. 5 (2008): 991–1000. http://dx.doi.org/10.5194/nhess-8-991-2008.

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Abstract. Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex non-linear relationships between the meteorological input conditions and each cluster of clouds. The patterns d

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10

Qin, Rongyuan. "The Construction of Corporate Financial Management Risk Model Based on XGBoost Algorithm." Journal of Mathematics 2022 (April 13, 2022): 1–8. http://dx.doi.org/10.1155/2022/2043369.

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Corporate financial management is a tedious task, and it is a complicated thing to rely solely on the human resources of financial personnel to manage. With the continuous development of intelligent algorithms and machine learning algorithms, new ideas have been brought to enterprise financial risk assessment. This method will not only save a lot of financial and material resources but also improve the accuracy of enterprise financial risk assessment. Compared with machine learning algorithms such as random forests and support vector machines, the extreme gradient boosting (XGBoost) algorithm

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11

Hobohm, Lukas, Kristian Hellenkamp, Gerd Hasenfuß, Thomas Münzel, Stavros Konstantinides, and Mareike Lankeit. "Comparison of risk assessment strategies for not-high-risk pulmonary embolism." European Respiratory Journal 47, no. 4 (2016): 1170–78. http://dx.doi.org/10.1183/13993003.01605-2015.

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We compared the prognostic performance of the 2014 European Society of Cardiology (ESC) risk stratification algorithm with the previous 2008 ESC algorithm, the Bova score and the modified FAST score (based on a positive heart-type fatty acid-binding protein (H-FABP) test, syncope and tachycardia, modified using high-sensitivity troponin T instead of H-FABP) in 388 normotensive pulmonary embolism patients included in a single-centre cohort study.Overall, 25 patients (6.4%) had an adverse 30-day outcome. Regardless of the score or algorithm used, the rate of an adverse outcome was highest in the

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12

Gonjari, Sneha. "Heart Disease Risk Assessment Using Machine Learning Algorithms." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 1525–29. https://doi.org/10.61137/ijsret.vol.11.issue2.264.

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13

Zhu, Juntao. "Application of Artificial Intelligence Data Mining Algorithm in Enterprise Management Risk Assessment." International Journal of Information Systems and Supply Chain Management 17, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijisscm.342119.

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For governmental and non-governmental enterprises to tackle risk management with conviction, enterprise management risk assessment (EMRA) is required. This work proposes a research methodology based on an AI-based data mining algorithm (MSVM+EFCNN) for evaluating enterprise-related risks. Initially, all the possible risk assessment indexes of the enterprise are established using a large variety of identification parameters. Then, the data mining algorithms are trained by considering the previous data for building an EMRA model. At last, the current conditions are analyzed using a cluster of ri

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14

Chesalin, A. N., S. Ya Grodzenskiy, Pham Van Tu, M. Yu Nilov, and A. N. Agafonov. "Technology for risk assessment at product lifecycle stages using fuzzy logic." Russian Technological Journal 8, no. 6 (2020): 167–83. http://dx.doi.org/10.32362/2500-316x-2020-8-6-167-183.

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The problem of risk assessment at the stages of the product life cycle using both qualitative and quantitative approaches is investigated, and a generalized algorithm for selecting a fuzzy risk assessment model with different input data and system requirements is proposed for the effective use of statistical information and expert assessments. The "risk-based approach" allows to reduce the cost of correcting possible errors in the future and reduce the uncertainty when performing subsequent actions. It is noted that the results of SWOT analysis, as a rule, are of a qualitative descriptive natu

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15

Huang, Boning, Junkang Wei, Yuhong Tang, and Chang Liu. "Enterprise Risk Assessment Based on Machine Learning." Computational Intelligence and Neuroscience 2021 (November 16, 2021): 1–6. http://dx.doi.org/10.1155/2021/6049195.

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Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the application of machine learning technology in enterprise risk assessment. According to the existing literature, this paper uses three machine learning algorithms, i.e., random forest (RF), support vector machine (SVM), and AdaBoost, to evaluate enterprise risk. In the specific implementation, the enter

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16

Chittari, Archana, Y.V. Sivareddy, and V. Sankar. "Risk assessment of electric power generation systems using modified jellyfish search algorithm." Transactions on Energy Systems and Engineering Applications 5, no. 2 (2024): 1–14. http://dx.doi.org/10.32397/tesea.vol5.n2.595.

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An electric utility's main goal is to fulfil the requirements and expectations of its customers by providing power. When there are uncertainties, like equipment failures, system reliability evaluation offers a framework to guarantee that the system will still function properly. A modified Jellyfish Search Algorithm (JFSA) has been proposed for estimation of Electric power generation system reliability indices. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and other modified versions of algorithms have been used in algorithms that use optimization methods for the assessment of reli

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17

Liu, Yu. "Discussion on the Enterprise Financial Risk Management Framework Based on AI Fintech." Decision Making: Applications in Management and Engineering 7, no. 1 (2023): 254–69. http://dx.doi.org/10.31181/dmame712024942.

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Deep learning algorithms lack interpretability and interpretability in the decision-making process. This makes it difficult to understand the judgment basis and decision-making process of financial risks based on algorithms, which may reduce the trust and acceptance of risk decisions by enterprises. To address this issue, this study introduces the improved random forest algorithm based on the decision tree algorithm to discuss its framework. Through analysis of the PR curve in the experiment, it was determined that the AP value of the enhanced random forest algorithm is 0.9919, a significant i

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18

Du, Juan, and Mingqi Guo. "Wireless Mobile Power Communication System Based on Artificial Intelligence Algorithm." International Transactions on Electrical Energy Systems 2022 (September 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/1636033.

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In order to solve the problems of low-risk assessment accuracy and long time-consuming assessment of current wireless mobile communication systems, a wireless mobile communication system based on artificial intelligence algorithms is proposed. First, the research status of risk assessment of wireless mobile communication system at home and abroad is analyzed, and the risk assessment index system of wireless mobile communication system is established; then, the learning samples are collected according to the risk assessment index system of wireless mobile communication system, and artificial in

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19

Xu, Tianyi. "Comparative Analysis of Machine Learning Algorithms for Consumer Credit Risk Assessment." Transactions on Computer Science and Intelligent Systems Research 4 (June 20, 2024): 60–67. http://dx.doi.org/10.62051/r1m3pg16.

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In the rapidly evolving landscape of financial technology, machine learning algorithms are increasingly supplanting traditional methodologies for evaluating consumer credit risk. This study leverages a comprehensive dataset comprising 10,000 credit accounts to conduct a comparative analysis of four prevalent machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). The results distinctly favor GBM, which achieves an AUC of 0.87, closely followed by Random Forest with an AUC of 0.85. In stark contrast, Logistic Regression and Decision T

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20

Hassani, Zeinab, Mohsen Alambardar Meybodi, and Vahid Hajihashemi. "Credit Risk Assessment Using Learning Algorithms for Feature Selection." Fuzzy Information and Engineering 12, no. 4 (2020): 529–44. http://dx.doi.org/10.1080/16168658.2021.1925021.

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21

Fitzgerald, Xavier, Ana Herceg, Kirsty Douglas, and Nadeem Siddiqui. "Cardiovascular disease risk assessment in an Aboriginal community-controlled health service: comparing algorithms." Australian Journal of Primary Health 26, no. 4 (2020): 281. http://dx.doi.org/10.1071/py19216.

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Aboriginal and Torres Strait Islander people have high rates of cardiovascular disease (CVD). The National Vascular Disease Prevention Alliance (NVDPA) CVD risk assessment algorithm is used for all Australians. The Central Australian Rural Practitioners Association (CARPA) algorithm used in the Northern Territory adds five percentage points to all NVDPA risk scores for Indigenous Australians. Information was extracted from an Aboriginal Community-Controlled Health Service for all Aboriginal and Torres Strait Islander regular clients aged 35–74 years without known CVD (n=1057). CVD risk scores

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22

Rajapandiyan, Mr P. "Wildfire Risk Assessment System." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04519.

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Abstract: This project aims to develop a predictive model for forest fire detection using historical fire datasets and weather report features. Leveraging Data Science and Machine Learning techniques, the model learns from past fire incidents—incorporating factors such as temperature, humidity, wind speed, and rainfall—to detect the likelihood of future fires. The system is built as a web application using Flask, offering real-time fire risk predictions through a simple user interface. The project pipeline includes data ingestion and cleaning, exploratory data analysis and visualization, featu

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23

Podgórski, Daniel. "Przegląd zastosowania algorytmów uczenia maszynowego do oceny i monitorowania ryzyka zawodowego w czasie rzeczywistym." Occupational Safety – Science and Practice 643, no. 4 (2025): 16. https://doi.org/10.54215/bp.2025.4.7.podgorski.

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Overview of the use of machine learning algorithms for real-time occupational risk assessment and monitoring Machine learning (ML) is an area of artificial intelligence dealing with algorithms that can modify their parameters based on processed data and perform tasks without explicit instructions. The development of artificial intelligence systems and Internet of Things technologies is resulting in ML algorithms being increasingly used in various sectors of the economy, including occupational risk assessment and monitoring. This article provides a review of the scientific literature in this fi

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24

Seidelin, Cathrine, Therese Moreau, Irina Shklovski, and Naja Holten Møller. "Auditing Risk Prediction of Long-Term Unemployment." Proceedings of the ACM on Human-Computer Interaction 6, GROUP (2022): 1–12. http://dx.doi.org/10.1145/3492827.

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As more and more governments adopt algorithms to support bureaucratic decision-making processes, it becomes urgent to address issues of responsible use and accountability. We examine a contested public service algorithm used in Danish job placement for assessing an individual's risk of long-term unemployment. The study takes inspiration from cooperative audits and was carried out in dialogue with the Danish unemployment services agency. Our audit investigated the practical implementation of algorithms. We find (1) a divergence between the formal documentation and the model tuning code, (2) tha

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25

Wu, Chuanwen, Shumei Zhang, Xiaoli Bao, Yang Wang, Zhikun Miao, and Huixin Liu. "Risk Assessment Approach of Electronic Component Selection in Equipment R&D Using the XGBoost Algorithm and Domain Knowledge." Processes 12, no. 8 (2024): 1716. http://dx.doi.org/10.3390/pr12081716.

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Risk management in electronic component selection is crucial for ensuring inherent system quality dependability in aerospace equipment research and development (R&D). Therefore, it is of great significance to conduct rapid and accurate risk assessment research of electronic components based on engineering practice. This article utilizes the extreme gradient boosting (XGBoost) algorithm and domain knowledge to assess electronic component selection risk. Firstly, an innovative risk assessment system is established for electronic component selection based on business materials analysis and in

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26

Titarenko, Boris, Amir Hasnaoui, Roman Titarenko, and Liliya Buzuk. "Project risk management in the construction of high-rise buildings." E3S Web of Conferences 33 (2018): 03074. http://dx.doi.org/10.1051/e3sconf/20183303074.

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This paper shows the project risk management methods, which allow to better identify risks in the construction of high-rise buildings and to manage them throughout the life cycle of the project. One of the project risk management processes is a quantitative analysis of risks. The quantitative analysis usually includes the assessment of the potential impact of project risks and their probabilities. This paper shows the most popular methods of risk probability assessment and tries to indicate the advantages of the robust approach over the traditional methods. Within the framework of the project

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27

Li, Jiehui. "Venture financing risk assessment and risk control algorithm for small and medium-sized enterprises in the era of big data." Journal of Intelligent Systems 31, no. 1 (2022): 611–22. http://dx.doi.org/10.1515/jisys-2022-0047.

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Abstract The existing risk assessment and control methods of enterprise risk financing have a large error in mobile data, which leads to inaccurate risk assessment results and low-risk optimization control efficiency. In order to improve the accuracy of risk financing risk assessment for small and medium-sized enterprises (SMEs) and risk control optimization efficiency, this article proposes risk assessment and risk control algorithms for SMEs in the era of big data. Through verifying the information of the loan application and supplementing the data during the loan period, invoke the existing

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28

Guo, Yi. "Risk prediction of computer investment database information management system based on machine learning algorithms." Molecular & Cellular Biomechanics 21, no. 4 (2024): 920. https://doi.org/10.62617/mcb920.

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In recent years, with the continuous development of the financial market, the risk prediction of computer investment database information management systems (IMS) has high practical value. At present, there are risk issues in the information management system, which may cause drawbacks to investment data processing. To address these issues, this article used Machine Learning (ML) algorithms to analyze the risk prediction of computer investment database IMS. This article introduced and utilized typical Self-Organizing Map (SOM) and Artificial Neural Network (ANN) combination algorithms, regress

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Prof. M. S. Patil, Benkar Anuradha, Gaikwad Madhuri, and Sawant Supriya. "CARDIO PREDICT: HARNESSING MACHINE LEARNING FOR ADVANCED HEART DISEASE RISK ASSESSMENT." International Journal of Innovations in Engineering Research and Technology 11, no. 4 (2024): 28–32. http://dx.doi.org/10.26662/ijiert.v11i4.pp28-32.

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Heart disease prediction using machine learning algorithms has gained significant attention due to its potential to improve diagnosis and treatment. This study explores various machine learning techniques and an algorithm applied to heart disease prediction. We analyze the performance of popular algorithms such as logistic regression, decision trees, random forests, support vector machines, and artificial neural networks on heart disease datasets. Additionally, we investigate the impact of feature selection, data preprocessing techniques, and model evaluation metrics on the predictive performa

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Wang, Yijie. "Research on Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural Networks." Wireless Communications and Mobile Computing 2021 (February 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/5565980.

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With the development of supply chain finance, the credit risk of small- and medium-sized financing enterprises from the perspective of supply chain finance has arisen. Risk management is one of the key tasks of the credit business of banks and other financial institutions, which runs through all aspects of the credit business before, during, and after the loan. This article combines blockchain and fuzzy neural network algorithms to study the credit risk of SME financing from the perspective of supply chain finance. This article builds a supply chain financial system through blockchain technolo

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Hamilton, Melissa. "Evaluating Algorithmic Risk Assessment." New Criminal Law Review 24, no. 2 (2021): 156–211. http://dx.doi.org/10.1525/nclr.2021.24.2.156.

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Algorithmic risk assessment is hailed as offering criminal justice officials a science-led system to triage offender populations to better manage low- versus high-risk individuals. Risk algorithms have reached the pretrial world as a best practices method to aid in reforms to reduce reliance upon money bail and to moderate pretrial detention’s material contribution to mass incarceration. Still, these promises are elusive if algorithmic tools are unable to achieve sufficient accurate rates in predicting criminal justice failure. This article presents an empirical study of the most popular pretr

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Yang, Yidan. "Application of Data Mining Algorithms in Bank Credit Risk Assessment: Review and Prospect." Applied and Computational Engineering 150, no. 1 (2025): 41–46. https://doi.org/10.54254/2755-2721/2025.22400.

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Against the backdrop of intensified competition in the financial market and increased demand for risk management and control, the wave of digital transformation is profoundly reshaping the landscape of the financial industry. The accumulation of massive amounts of data has brought new opportunities and challenges for financial institutions to innovate business models and enhance risk management capabilities. This paper focuses on the application of data mining algorithms in bank credit risk assessment. Through literature review and case analysis, it explores the application status, effects, ch

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33

Scurich, Nicholas, and Daniel A. Krauss. "Public’s views of risk assessment algorithms and pretrial decision making." Psychology, Public Policy, and Law 26, no. 1 (2020): 1–9. http://dx.doi.org/10.1037/law0000219.

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Xu, Tianyi. "Fraud Detection in Credit Risk Assessment Using Supervised Learning Algorithms." Computer Life 12, no. 2 (2024): 30–36. http://dx.doi.org/10.54097/qw9j1892.

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Our study systematically evaluates the performance of various supervised learning algorithms in credit risk assessment and fraud detection, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Gradient Boosting Tree, and Neural Network. The results show that in credit risk assessment, the Gradient Boosting Tree performed best with an accuracy of 90.5% and a ROC-AUC of 0.84, followed by Random Forest and Neural Network, with accuracies of 89.2% and 88.8%, and ROC-AUCs of 0.82 and 0.81, respectively. In the fraud detection task, the Neural Network performed best w

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Pivovarova, I. I., R. D. Terekhin, S. V. Sarkisov, A. A. Sorokin, and V. I. Musatov. "Software implementation of fuzzy logic algorithms for environmental risk assessment." Journal of Physics: Conference Series 1515 (April 2020): 022091. http://dx.doi.org/10.1088/1742-6596/1515/2/022091.

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Deepa Shukla. "A Survey of Machine Learning Algorithms in Credit Risk Assessment." Journal of Electrical Systems 20, no. 3 (2024): 6290–97. https://doi.org/10.52783/jes.6788.

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Credit risk assessment is a critical process for financial institutions, designed to predict the likelihood of borrower default and reduce potential financial losses. Traditionally, credit scoring relied on statistical models; however, the advent of machine learning (ML) has significantly transformed these methods. Machine learning provides more accurate, scalable, and flexible solutions for analyzing vast amounts of financial data. This survey examines key ML algorithms—including decision trees, random forests, support vector machines, and deep neural networks—that are used in credit risk ass

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CHRIS GILBERT and MERCY ABIOLA GILBERT. "Data Encryption Algorithms and Risk Management." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 3 (2025): 479–507. https://doi.org/10.51583/ijltemas.2025.140300054.

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Abstract: In today's digital era, data is increasingly viewed as the new "green gold," making robust information security more critical than ever. This research investigates modern encryption algorithms alongside comprehensive risk management strategies to protect enterprise data. By systematically comparing symmetric, asymmetric, and hash-based encryption methods, the study reveals the inherent trade-offs between computational performance and security. A multifaceted methodology—combining literature review, algorithmic evaluation, multivariate risk assessment, and real-world case studies—is e

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Dubois, Amandine, Titus Bihl, and Jean-Pierre Bresciani. "Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms." Sensors 21, no. 6 (2021): 1957. http://dx.doi.org/10.3390/s21061957.

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Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients’ movements while they were going about their daily

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Yadav, Shivani. "Heart Disease Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem36858.

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Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the ac

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Wu, Ying, and Pengzhen Lu. "Comparative Analysis and Evaluation of Bridge Construction Risk with Multiple Intelligent Algorithms." Mathematical Problems in Engineering 2022 (June 15, 2022): 1–12. http://dx.doi.org/10.1155/2022/2638273.

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Bridge collapse in the construction process is one of the most common bridge accidents; we can see the importance of construction to ensure bridge safety. Site construction safety management mainly relies on checklist evaluation, but the evaluation results are often affected by the ability and experience of the evaluator. Using an artificial intelligence algorithm to realize rapid and accurate risk assessment of the bridge construction process is an effective way and development direction to solve the above problems. In view of the uncertain factors in the process of bridge construction, this

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Garcha, Ivneet, and Susan Paula Phillips. "Social bias in artificial intelligence algorithms designed to improve cardiovascular risk assessment relative to the Framingham Risk Score: a protocol for a systematic review." BMJ Open 13, no. 5 (2023): e067638. http://dx.doi.org/10.1136/bmjopen-2022-067638.

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IntroductionCardiovascular disease (CVD) prevention relies on timely identification of and intervention for individuals at risk. Risk assessment models such as the Framingham Risk Score (FRS) have been shown to over-estimate or under-estimate risk in certain groups, such as socioeconomically disadvantaged populations. Artificial intelligence (AI) and machine learning (ML) could be used to address such equity gaps to improve risk assessment; however, critical appraisal is warranted before ML-informed clinical decision-making is implemented.Methods and analysisThis study will employ an equity-le

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Huang, Hui, and Thien Sang Lim. "Construction and Optimization of Financial Risk Management Model Based on Financial Data and Text Data Influencing Information System." Journal of Information Systems Engineering and Management 9, no. 2 (2024): 24534. http://dx.doi.org/10.55267/iadt.07.14767.

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A-share companies must manage financial risk to succeed. Textual data insights can greatly impact risk assessment results, although most risk management systems focus on quantitative financial assessments. This research constructs and enhances information system financial risk management models employing financial and textual data, including MD&amp;A narratives, to fill this gap. We study how textual data aids financial risk management algorithms' risk prediction. Textual and financial research on 2001–2022 Shenzhen and Shanghai Stock Exchange companies is used. This study found financial

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Greene, Barry R., Killian McManus, Lilian Genaro Motti Ader, and Brian Caulfield. "Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning." Sensors 21, no. 14 (2021): 4770. http://dx.doi.org/10.3390/s21144770.

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Assessment of health and physical function using smartphones (mHealth) has enormous potential due to the ubiquity of smartphones and their potential to provide low cost, scalable access to care as well as frequent, objective measurements, outside of clinical environments. Validation of the algorithms and outcome measures used by mHealth apps is of paramount importance, as poorly validated apps have been found to be harmful to patients. Falls are a complex, common and costly problem in the older adult population. Deficits in balance and postural control are strongly associated with falls risk.

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Fine, Anna, Stephanie Le, and Monica K. Miller. "Content Analysis of Judges' Sentiments Toward Artificial Intelligence Risk Assessment Tools." Journal of Criminology, Criminal Justice, Law & Society 24, no. 2 (2023): 31–46. http://dx.doi.org/10.54555/ccjls.8169.84869.

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Artificial intelligence (AI) uses computer programming to make predictions (e.g., bail decisions) and has the potential to benefit the justice system (e.g., save time and reduce bias). This secondary data analysis assessed 381 judges’ responses to the question, “Do you feel that artificial intelligence (using computer programs and algorithms) holds promise to remove bias from bail and sentencing decisions?” The authors created apriori themes based on the literature, which included judges’ algorithm aversion and appreciation, locus of control, procedural justice, and legitimacy. Results suggest

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Dimitrakopoulos, Ioannis, Evanthia Asimakopoulou, Alexander Argyriadis, and Maritsa Gourni. "Presentation of risk assessment models for cardiovascular disease." Rostrum of Asclepius 19, no. 3 (2020): 183–97. https://doi.org/10.5281/zenodo.3926682.

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<strong>Introduction: </strong>Cardiovascular disease (CVD) is the main cause of mortality worldwide and an important public health issue with a serious social and economic impact. Cardiovascular risk assessment models (long and short term) have been developed since 1950, which are based on the examination of various risk factors. <strong>Aim: </strong>To present indicative models of cardiovascular risk, to compare and apply them in clinical practice. <strong>Methodology: </strong>A review of the literature was performed on the databases of Google Scholar, Scopus, Science Direct, and PubMed. T

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Schumacher, Connie, Margaret Saari, Fabrice Mowbray, et al. "Comprehensive Standardized Assessment for Information Continuity: What Does the Workforce Need." International Journal of Integrated Care 23, S1 (2023): 771. http://dx.doi.org/10.5334/ijic.icic23634.

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Introduction: Older adults living with frailty and multimorbidity interact with multiple care providers across different health settings increasing the risk for fragmented care and information discontinuity. Information discontinuity results in workforce inefficiencies and adverse health events, including duplication of assessment and diagnostics, medication errors and increased health service use. Standardized assessments potentiate integrated care by communicating consistent measures of health information between health care sectors and providers. InterRAI assessments facilitate integration

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Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.

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The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total

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Begun, V. V., and S. Yu Potetiuiev. "New method for fire risk assessment." Mathematical machines and systems 4 (2020): 125–35. http://dx.doi.org/10.34121/1028-9763-2020-4-125-135.

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Ukraine is at the last places at the international ratings concerning safety of life. It is related first of all to the fields of technogenic safety and fire safety. The values of annual losses due to fires, accidents and due to other emergency situations reach billions of hryvna, which is the essential part of the national budget and they have the increasing trends. Such situation is common for almost all branches of the industry of Ukraine except for the nuclear energy of Ukraine, which is under the additional guidance and under the additional control of the international organizations. The

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Bai, Yunpu, and Dunlin Zha. "Commercial Bank Credit Grading Model Using Genetic Optimization Neural Network and Cluster Analysis." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/4796075.

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Commercial banks are facing unprecedented credit risk challenges as the financial market becomes more volatile. Based on this, this study proposes and builds a credit risk assessment model for commercial banks based on GANN from the standpoint of commercial banks. In order to provide commercial banks with an effective and dependable credit risk assessment method, the indicators in this study are classified using cluster analysis, and then various representative indicators are chosen using a factor model, which takes into account the comprehensiveness of the information and reduces the complexi

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Chandurkar, Mrs Swati. "Comparative Study of Machine Learning Algorithms for Loan Default Prediction." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2661–68. https://doi.org/10.22214/ijraset.2025.67894.

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Анотація:

This review paper compares machine learning algorithms for loan default prediction, focusing on preprocessing techniques, Random Forest, Gradient Boosting Machine (GBM), Naive Bayes, and visualization methods. It highlights the critical role of these algorithms in financial risk assessment. The study evaluates their performance, strengths, weaknesses, and suitability for different data types. It emphasizes the importance of selecting the right algorithm based on dataset characteristics. The findings emphasize advanced machine learning's significance in improving risk management and lending dec

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