Use Of Predictive Analytics Research Articles

Artificial Intelligence And Big Data For Enhancing Public Health Surveillance And Disease Prevention: A Systematic Review

This systematic review explores the transformative role of Artificial Intelligence (AI) and Big Data analytics in enhancing public health outcomes, focusing on key areas such as disease surveillance, resource allocation, and personalized preventive healthcare. In the wake of increasing healthcare challenges, the integration of AI technologies and Big Data offers unprecedented opportunities for improving health monitoring, early disease detection, and strategic resource management. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review was conducted across multiple databases, resulting in an initial pool of 450 articles. After applying rigorous inclusion and exclusion criteria, a total of 90 high-quality studies were systematically analyzed. The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic. Additionally, the use of predictive analytics in personalized preventive healthcare has shown promise in reducing the burden of chronic diseases by identifying at-risk populations and tailoring interventions based on individual risk profiles. However, challenges related to data quality, standardization, and ethical concerns continue to hinder the widespread adoption of these technologies. The review emphasizes the need for interdisciplinary collaboration and robust data governance frameworks to fully realize the potential of AI and Big Data in public health. This study not only highlights current advancements but also identifies gaps in research, offering insights into future directions for integrating AI-driven solutions to strengthen public health systems globally.

Harnessing AI and Predictive Analytics to Revolutionize Customer Retention Strategies

Artificial Intelligence (AI) and predictive analytics are rapidly transforming how businesses approach customer retention, enabling more proactive and personalized strategies. This research investigates the role of AI-driven predictive analytics in identifying at-risk customers, forecasting churn, and optimizing retention efforts across various industries. By analyzing historical data, machine learning models can accurately predict future customer behavior, enabling businesses to implement targeted retention strategies such as personalized offers, timely engagement, and customized support. This study explores how AI-powered tools enhance customer segmentation, allowing marketers to identify the factors that contribute to customer attrition and loyalty. Moreover, the research delves into the use of predictive analytics in monitoring customer interactions, identifying warning signs of dissatisfaction, and responding with interventions before churn occurs. Ethical concerns, such as the balance between personalization and privacy, will also be addressed, particularly regarding how businesses can maintain consumer trust while leveraging personal data for predictive purposes. Through case studies and industry analysis, this research aims to demonstrate the significant advantages of incorporating AI and predictive analytics into customer retention strategies. It will provide insights into best practices for utilizing these technologies to enhance customer loyalty, improve satisfaction, and ultimately drive sustainable business growth. this research examines the practical applications of AI and predictive analytics across different business sectors, such as e-commerce, telecommunications, and financial services. By integrating AI-powered tools into customer relationship management (CRM) systems, companies can develop more effective retention strategies that are both data-driven and customer-centric. This includes deploying AI algorithms to monitor customer lifetime value (CLV) and predict the likelihood of repeat purchases, helping businesses prioritize high-value customers while minimizing churn rates among at-risk segments.

BIG DATA AND CHRONIC DISEASE MANAGEMENT THROUGH PATIENT MONITORING AND TREATMENT WITH DATA ANALYTICS

This systematic review investigates the role of predictive analytics in chronic disease management, focusing on its capacity to predict disease progression, reduce hospitalizations, and enhance patient outcomes. A total of 35 peer-reviewed articles were analyzed to evaluate how predictive models, utilizing data from electronic health records (EHRs), wearable devices, and real-time monitoring systems, are applied in managing chronic diseases such as diabetes and cardiovascular conditions. The findings indicate that predictive models have significantly improved early disease detection accuracy, with studies showing improved forecasting of heart failure exacerbations and notable reductions in hospital readmissions due to timely interventions. Additionally, many studies reported a reduction in healthcare utilization through predictive analytics-driven early interventions. However, challenges regarding data quality and model accuracy were frequently cited, particularly concerning data integration and harmonization across various healthcare systems. Ethical and privacy concerns, including data security and algorithmic bias, were also highlighted, underscoring the need to address these issues for responsible and equitable use of predictive analytics. This review affirms the growing impact of predictive analytics in chronic disease management, while calling for advancements in data management and ethical frameworks to maximize its potential.

AI-Powered customer experience optimization: Enhancing financial inclusion in underserved communities

The integration of Artificial Intelligence (AI) into customer experience optimization holds transformative potential for advancing financial inclusion in underserved communities. This review explores how AI-driven tools can enhance personalized banking services, streamline customer engagement, and improve access to financial products for individuals who are often marginalized by traditional banking systems. By leveraging AI algorithms, financial institutions can analyze vast amounts of customer data to identify unique needs, preferences, and behaviors, enabling the delivery of tailored financial solutions. These AI-powered systems can also automate routine tasks, such as account management and customer support, ensuring that banking services are more accessible, efficient, and cost-effective. A key benefit of AI in this context is its ability to overcome barriers such as geographic isolation and lack of financial literacy, which often hinder financial inclusion. AI-driven chatbots, for instance, can provide 24/7 support in local languages, guiding users through complex financial processes. Moreover, AI enables the creation of personalized financial literacy programs, tailored to the specific needs of underserved populations, fostering a deeper understanding of financial products and encouraging greater participation in the financial system. Additionally, AI-powered fraud detection systems enhance trust and security in financial transactions, a critical factor in encouraging participation from communities traditionally skeptical of formal banking systems. The use of predictive analytics further supports the development of micro-loans and credit products suited to the financial realities of low-income individuals, reducing default rates and improving access to capital. In conclusion, AI-powered customer experience optimization offers a powerful strategy for financial institutions seeking to expand their reach into underserved communities. By providing personalized, efficient, and secure banking experiences, AI can significantly contribute to financial inclusion efforts, driving economic empowerment and reducing inequalities. This study highlights the critical role of AI in reshaping financial services and creating inclusive opportunities for underserved populations. Keywords: Artificial Intelligence, Financial Inclusion, Customer Experience Optimization, Underserved Communities, Personalized Banking, AI-Powered Tools, Financial Literacy, Fraud Detection, Predictive Analytics, Economic Empowerment.

FRAUD DETECTION IN FINANCIAL TRANSACTIONS THROUGH DATA SCIENCE FOR REAL-TIME MONITORING AND PREVENTION

This study presents a comprehensive review of the use of advanced technologies in credit card fraud detection, with a focus on machine learning, blockchain, and federated learning, to understand their transformative impact on the field. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 97 articles were systematically reviewed and analyzed. The findings reveal that machine learning models, such as decision trees, support vector machines, and neural networks, have significantly improved fraud detection accuracy, reducing false positives and enhancing the ability to detect complex fraud patterns in real-time. Blockchain technology also plays a critical role by providing a decentralized, secure, and transparent framework for fraud detection, ensuring the integrity of transaction records and making fraudulent activities harder to conceal. Federated learning offers a privacy-preserving solution, enabling institutions to collaborate on fraud detection without sharing sensitive data, which is increasingly important in light of stringent regulatory requirements. Additionally, the study highlights the growing use of predictive analytics in forecasting potential fraud, allowing financial institutions to proactively prevent fraud before it occurs. Moreover, feedback loops integrated into fraud detection models allow for continuous improvement, ensuring that detection systems can adapt to new and evolving fraud tactics. Overall, the review underscores the importance of adopting these advanced technologies to build more secure, efficient, and adaptive fraud detection systems capable of safeguarding financial transactions in the modern digital economy.

ПРИМЕНЕНИЕ ИНСТРУМЕНТОВ ПРЕДИКТИВНОЙ АНАЛИТИКИ ДЛЯ ПРОГНОЗА ВОССТАНОВЛЕНИЯ КРУПНОГАБАРИТНОГО ОБОРУДОВАНИЯ

The article presents the results of a study in the field of predictive analytics for the repair of largesized equipment in the building materials industry. New data processing techniques and the use of artificial intelligence,especially machine learning, can improve the prediction of the future state of equipment. The stages of predictive analytics application are presented, the possibility of predicting the residual resource is described and the effective use of predictive analytics is demonstrated using the example of restoring the trunnion of a ball mill. The data obtained are analyzed, on the basis of which it is concluded that repair work is necessary to increase the inter-repair life of the equipment and reduce economic losses. In conclusion, the validity of the use of predictive analytics for the repair of large-sized equipment and making optimal decisions is emphasized. The use of predictive analytics makes it possible to increase the efficiency of repair of large-sized equipment by increasing the repair life, reducing the number of downtime and preventing unplanned breakdowns. This approach is a promising and practical solution for enterprises in the building materials industry.

Predictive analytics, operational efficiency, and revenue growth in SMEs in Africa

Predictive analytics (PA) is used for a variety of objectives, including optimisation, planning, strategy development, and resource management, all to improve performance. PA also helps organisations and customers communicate more effectively. Managers may increase customer satisfaction by integrating data on consumer preferences and experiences. Despite increased interest in PA technologies, there is still a considerable study gap in understanding its influence on small and medium-sized enterprises (SMEs) operational efficiency and revenue growth in Sub-Saharan Africa. Hence, the survival of SMEs as key drivers of economic growth and development in Sub-Saharan necessitated this study. The study investigates the influence of predictive analytics on operational efficiency and revenue growth of SMEs in Accra, Ghana, and Lagos, Nigeria. Acknowledging both cities as main commercial centres in their respective nations Using a cross-sectional and survey research approaches, the study sent 150 copies of questionnaire to SMEs in each of the cities using purposive sampling techniques, therefore guaranteeing a focus on key economic centres. Under the guidance of four research assistants in each city monitoring distribution and collection, data were gathered online over two months. The results showed that predictive analytics has positive and significant effect on both operational efficiency and revenues growth of SMEs. The study recommended that SMEs should give training to their staff top priority as well as build the required infrastructure to make the best use of predictive analytics. This entails not just purchasing cutting-edge analytics tools and technology but also making sure staff members have the ability to understand and act upon data-driven insights. Building a solid basis in predictive analytics can help SMEs better use data to maximise operations, foresee trends, and make wise strategic choices, thereby promoting higher efficiency and income development

Trends and Perspectives in Enhancing the Competitiveness of Slovak Businesses Through Predictive HR Analytics

Abstract The use of HR analytics has been on the rise in recent years, with organizations increasingly recognizing its potential to improve HR processes, increase employee productivity and engagement, and reduce costs. The research presented in this paper extends the knowledge base, especially the characteristics of the degree of implementation of HR analysis into working systems of human resources management utilized within businesses operating within the Slovakian context, emphasizing their role in bolstering and enhancing competitiveness within the European economic arena. Although there was a clear interest in the use of predictive analytics in Slovak companies, there was still a lot of room for improvement and adoption of this approach in HR practice. The authors' findings also suggest that companies in Slovakia are increasingly aware of the value of data-driven decision-making in HR and are willing to invest in these technologies to gain a competitive advantage. The objective of this study is to ascertain contemporary human resource management instruments utilized within businesses operating within the Slovakian context. The authors assumed that the perceived importance of the data approach in HR among Slovak companies is strong, and companies are open to learning more about this approach. A sample of 841 respondents was collected throughout 2020, sample included enterprises from the Slovak Republic. The interviews were conducted via phone in November 2022. The interview respondents are 7 HR representatives. The authors' findings suggest that companies in Slovakia are increasingly aware of the value of data-driven decision-making in HR and are willing to invest in these technologies to gain a competitive advantage.

Use of predictive analytics for the screening of patients with adolescent idiopathic scoliosis with quantitative gait analysis

Use of predictive analytics for the screening of patients with adolescent idiopathic scoliosis with quantitative gait analysis

Predictive Analytics for Preventive Medicine: Analyzing how Predictive Analytics is Utilized for Forecasting Patient Health Trends and Preventive Disease

The abstract is about the possible use of predictive analytics for the prevention of the disease, thereby fortifying patient health trends and preparing for disease.

Predictive Analytics for Identifying High-Risk Medicare Patients: Enhancing Preventive Care

The intended purpose of this paper is to carry out an analysis of the use of predictive modeling for the early identification of high-risk Medicare patients with the aim of enhancing preventative measures. MEDI-CARE, a vital policy in the United States of America, which focuses on offering health care services to the old and people with disabilities, has been criticized over such factors as Shortcoming of Medicare hence can be attributed to factors such as the growing costs of health care and the emerging cases of chronic diseases. A new type of risk stratification: The use of predictive analytics, which incorporates ML-AI, is also in the early warning system and to monitor or follow up on the patient’s adverse health event, subsequent to which care coordination plans can be formulated. The abstract, hence, is an explanation of the roles of preventive and predictive analytics, especially in health care, with such a thing as the identification of early high-risk patients and management. They are that there is a rise in the dependence on methodologies that are data-driven for the purpose of attaining enhanced healthcare outcomes, reduction in the hospitalization quotient and the effective management of diseases which occur chronically. A few such points comprise data and information from electronic health records and other digital healthcare databases, model construction, and model application. It also addresses the various ethical pitfalls likely to arise when using the concept of predictive analytics, and these include the privacy of the patient’s data, how the collected data will be protected and filtered, as well as the prejudice that might be experienced when developing the algorithm. Current and earlier models of prediction, as well as their application in Medicare and research limitations, are described in the literature review of the article. Whereas in the methodology section, the procedures used in data collection, modeling, and validation are described, the results and discussion section put into perspective the effectiveness of the work done in enhancing Preventive care through predictive analytics. Finally, the discussion in the conclusion shall be based on the findings of the study and recommend future research directions for policy making.

Implementing Predictive Analytics for Proactive Revenue Cycle Management

This research investigates the deployment of predictive analytics in the revenue cycle management (RCM) system in health-care organizations. In other words, adopting predictive analytics strategies that are more proactive rather than only the reactive approach has the potential of greatly increasing the revenue capture, decreasing the denial rates, and increasing the efficiency of operation. The analysis of the literature and the results of the research indicate that the RCM benefits from the use of predictive analytics; all the while having acknowledged some challenges, including data integration and the requirement for professionals that understand the field.

A perspective on smart universities as being downsized smart cities: a technological view of internet of thing and big data

The integration of internet of things (IoT) and big data technologies is transforming the overall perspective of managing various sectors of modern life; with higher educational sectors being no exception of this transformation. This paper explores the idea of a “smart university” as an extension of the overarching “smart city” framework, emphasizing the blending of IoT and big data technologies within higher education institutions. The study investigates the incorporation of IoT technologies throughout university campuses, including intelligent classrooms, smart infrastructure, and device networking. Moreover, the paper delves into the substantial role played by big data analytics in processing and extracting meaningful insights from extensive data generated by IoT devices in a Smart University. The use of predictive analytics, machine learning algorithms, and data-driven decision-making contributes to personalized learning experiences, adaptive campus management, and proactive maintenance of university facilities. Furthermore, this paper not only emphasizes the potential benefits of deploying IoT and big data in a university setting but also addresses challenges related to security, privacy, and ethical considerations. By embracing a comprehensive approach to technology integration, universities can leverage the capabilities of IoT and big data to establish intelligent, interconnected, and flexible educational environments that align with the broader vision of a smart city.

Data-Driven Investment Strategies in International Real Estate Markets: A Predictive Analytics Approach

The study investigates the application of advanced predictive analytics in formulating investment strategies for the international real estate market. Utilizing extensive datasets, including real estate transaction records, economic indicators, and market reports, covering over ten years of data from 2010 to 2020 across multiple regions, we implemented predictive models such as linear regression, decision trees, random forests, support vector machines (SVM), neural networks, and gradient boosting machines (GBM). The results indicate that AI and machine learning models significantly outperform traditional statistical methods in forecasting market trends. Specifically, the neural network model achieved an R² of 0.822, while the random forest model attained an R² of 0.804, compared to an R² of 0.751 for the traditional linear regression model. Performance varied across regions and property types; for instance, the neural network model's MAE and RMSE in North America were 17,500 and 26,800, respectively, whereas in the Asia-Pacific region, the MAE and RMSE were 20,100 and 29,800. Additionally, these models resulted in an average reduction of 12.5% in operational costs and an 18.3% improvement in customer satisfaction. This study systematically integrates and compares multiple advanced predictive models, demonstrating that data-driven investment strategies offer significant competitive advantages in the real estate sector. These findings provide robust evidence supporting the use of predictive analytics to optimize investment decisions and highlight the transformative impact of these technologies on the real estate industry.

THE ROLE OF PREDICTIVE ANALYTICS IN OPTIMIZING SUPPLY CHAIN RESILIENCE: A REVIEW OF TECHNIQUES AND CASE STUDIES

This study investigates the transformative impact of predictive analytics on enhancing supply chain resilience (SCR). Employing a systematic literature review and content analysis, the research aims to explore the integration, challenges, and strategic implications of predictive analytics within the supply chain ecosystem. Focusing on literature from 2014 to 2023, the study synthesizes insights from peer-reviewed articles and conference papers, adhering to stringent inclusion and exclusion criteria to ensure relevance and recency. The findings reveal that predictive analytics significantly contributes to supply chain agility, flexibility, and responsiveness, thereby bolstering SCR against disruptions. Key challenges identified include data privacy concerns, the need for skilled personnel, and the integration of predictive analytics into existing supply chain frameworks. Despite these challenges, the future outlook for predictive analytics in SCM is promising, with potential for unprecedented efficiency, sustainability, and competitive advantage. Strategic recommendations for practitioners emphasize the importance of developing predictive analytics capabilities, prioritizing data governance, and continuous staff training. For policymakers, the study suggests the need for standards and regulations that encourage innovation while ensuring the ethical use of predictive analytics. Finally, predictive analytics is pivotal in revolutionizing SCM, offering pathways for innovation, efficiency, and enhanced resilience. Future research should focus on the integration of emerging technologies, ethical data use frameworks, and overcoming adoption barriers, particularly in SMEs and developing economies. This study underscores the critical role of predictive analytics in the future of SCM, driving forward the agenda for research and practice in this evolving field.
 Keywords: Predictive Analytics, Supply Chain Resilience, Systematic Literature Review, Strategic Implications.

The Transformative Impact of Big Data in Healthcare: Improving Outcomes, Safety, and Efficiencies

The integration of Big Data analytics into the healthcare sector has initiated a period of unparalleled transformation, significantly influencing patient care, research, and healthcare administration. This detailed synthesis, derived from a systematic literature review of 31 articles published between 2013 and 2023, examines the extensive impact of Big Data within the healthcare industry. It addresses how Big Data enhances personalized medicine and optimizes treatment protocols, promotes the use of predictive analytics for the early detection and management of diseases, and bolsters patient safety through the implementation of real-time monitoring and decision support systems. The review also confronts the challenges and limitations associated with Big Data's application, including concerns over data privacy and security, ethical dilemmas, interoperability issues, and the necessity for both skilled personnel and advanced technological infrastructure. Furthermore, this overview ventures into future prospects and technological advancements, emphasizing the pivotal role of emerging technologies such as artificial intelligence (AI), machine learning, and blockchain in heralding a new era of healthcare. It highlights Big Data's instrumental role in supporting public health measures and preparing for pandemics, alongside forecasting the ongoing influence of Big Data in fostering innovative healthcare paradigms. This analysis advocates for a concerted effort among healthcare professionals, policymakers, and technology experts to fully leverage Big Data's capabilities in healthcare, aiming to enhance health outcomes, increase operational efficiencies, and ensure the long-term viability of healthcare systems.

Study of the role that AI can play in the Sustainable Fashion Business

This study investigates the potential application of artificial intelligence (AI) in the field of sustainable fashion by streamlining the production of clothing and incorporating trend analysis. By researching current fashion trends and consumer preferences to identify the most well-liked apparel patterns and designs, the article seeks to optimise supply and demand while reducing surplus production. Fashion garment production can be optimised to fulfil customer demand while minimising waste and lowering costs by incorporating trend research into the manufacturing process. Companies may now invest in clothing concepts that will sell thanks to AI trend forecasting, which reduces some of the uncertainty and human error that now hinder trend forecasting. The fashion industry is infamous for its detrimental effects on society and the environment, including creation of waste and pollution. While the fashion industry has started to adopt more sustainable practises, AI has the ability to quicken the process by offering creative answers to some of the sector's most pressing problems. The study looks at a number of AI-related case studies in the fashion sector, including the use of predictive analytics to cut waste and streamline supply chains, computer vision to enhance textile recycling, and natural language processing to encourage openness and moral work practises. The paper also examines how AI could revolutionise the fashion business and hasten the shift to a more ethical and sustainable future. The report also highlights the necessity for careful evaluation of these concerns as the fashion industry develops by posing significant ethical and social implications for AI.

Use of Predictive Analytics in Antimicrobial Resistance: A Review

Use of Predictive Analytics in Antimicrobial Resistance: A Review

Predictive analytics for patient adherence : A machine learning approach in medication management

In this paper, we investigate the use of predictive analytics and machine learning to improve patients’ adherence to prescribed medications. By analyzing past patient data, we are able to create a model that may foresee adherence behaviors based on characteristics like age, gender, health status, and income. In order to analyze and identify major drivers of non-adherence and enable personalized therapies, our method incorporates cutting-edge machine learning techniques. The methodology takes into account each individual’s situation while making suggestions, leading to a more proactive and specific strategy for improving drug adherence. Predictive analytics allows healthcare providers to foresee adherence issues, intervene at the right time, and boost patient outcomes. This research contributes to the developing landscape of healthcare by utilizing the potential of machine learning for precision medicine in drug management, addressing a vital part of patient care and public health.

Evolutionary algorithms for dynamic optimization of plug-in charging station networks

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of IoT on urban mobility, using empirical data obtained from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its alignment with actual data. The data reveals a link between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of anticipating congestion and AQI, hence confirming the effectiveness of the models. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban mobility, enhancing decision-making processes, and creating sustainable urban environments via the use of data-driven insights and proactive interventions.