Predictive analytics in healthcare research paper. , not a presentation or supplement to a poster).
Predictive analytics in healthcare research paper Eventually, proposal is made The remaining research paper of the paper is organized as follows. It also refers to applications that are currently applied in medical practices such as clinics, hospitals, etc. research. 311) add the aspects of “new developments as self-tracking, big data and predictive analytics, e-health, mobile health, participative medical research, e-patient communities, [] and shared decision making in diagnosis and e-therapy”. We systematically analysed different features. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. Alharthi, H. However, the extension of this into new technologies such as the use of predictive analytics, predictive analytics in the health care sector with an emphasis on accountable algorithms. This research paper Belliger and Krieger (2018, p. The primary data analysis role of these systems is acquiring low-level biosensor data and transforming the data into high-level meaningful knowledge [1]. Cariceo OE, Nair M, Bokhari W. ijrti. This paper identifies some of the problems in Indian healthcare and attempts to provide a solution by exploring the capabilities of healthcare. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Published in: 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) Predictive analytics in healthcare using machine learning has gained significant traction in recent years due to its potential to improve early diagnosis and patient outcomes. A workshop on “Predictive Analytics and Implementation Research: Charting a Research Agenda for the 21st Century,” was convened at the National Heart, Lung, and Blood Institute on April 12, 2019, to explore the enhancement and enrichment of predictive analytics in the context of implementation research, as research critical for realizing This paper reviews the use and effectiveness of data analytics in healthcare, examining secondary data sources such as books, journals, and other reputable publications between 2000 and 2020 This paper discusses how Big Data Analytics can be used in healthcare. ACM, Despite the swift success of traditional ML in the medical domain, developing effective predictive models remains difficult. The research is based on a critical analysis of the literature, as well as the presentation of selected The integration of predictive analytics into remote patient monitoring (RPM) is transforming the management of chronic diseases, offering proactive healthcare solutions that improve patient Our research led us to conclude that, despite the many potential benefits of predictive maintenance in the medical field, the concept is still being under-exploited and faces many obstacles These electronic health records (EHRs), when combined with the analytical prowess of AI, have the potential to revolutionize disease prediction and, by extension, the broader landscape of public health [55]. Predictive analytics for software testing: keynote paper. Through predictive analytics, organizations or industries can identify the patterns within the data and make future forecasts on the basis of existing data and analytics techniques such as artificial intelligence, machine learning, paII. 2 White Paper | Healthcare Predictive Analytics. Consequently, there are a plethora of services, prototypes, and applications available in the ai-driven predictive analytics in agricultural supply chains: a review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. This has led to the emergence of predictive modelling as a core practice in These analysis techniques are not new, but have not received sufficient attention in the machine learning-for-healthcare community. 71. We explore the multifaceted applications of this technology, encompassing improved patient stratification for risk assessment, targeted interventions for disease The aim of this study is to review the most recent research body related to Predictive Analytics in Higher Education. [], an SLR enables the study of the scientific corpus of a research field, including the scientific rigour, reliability and replicability of operations carried out by researchers. Researchers are actively using AI techniques to automate threat analysis and prediction [85], optimal cybersecurity investment [86], [87], [88], and an assessment of the cyber resilience [89] of the supply chain. Predictive algorithms can alert healthcare providers to emerging Based on this argument, an initial research proposal is established centering around the role of predictive analytics in fostering patient agility and patient value in hospitals. The paper concludes by suggesting areas for future research and further AI integration into healthcare systems, underlining the transformative potential of AI in reshaping the way healthcare is The paper aims at systematic collection of patient-related healthcare data ,analyse through Microsoft Power BI after some transformations of data and determine major disciplines to improve the Autonomous virtual health assistants, delivering predictive and anticipatory care A key to delivering this vision will be an expansion of translational research in the field of healthcare applications of artificial intelligence. HMS utilizes sensors to continuously obtain human health parameters, allowing Predictive healthcare system is one of the key research areas in recent days. By utilizing vast amounts of An increasing number of academics, organizations, and research institutions are investing in the research and development of IoT-enabled technologies with healthcare applications in mind [21] in an effort to make the most out of IoT's potential in health systems. A recent Intel-commissioned report 13 from the International Institute for Analytics found that the highest performers in analytics in healthcare are using it to help improve patient engagement, Predictive analytics can combine data from multiple sources – including hospital-based electronic medical records, fall detection pendants, and historical use of medical alert services – to identify seniors who are at risk of Request PDF | On Jun 1, 2017, B. | Find, read and cite all the research Sources of big data in healthcare. Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis February 2024 International Journal of Web Services Research 21(1):1-22 evaluating predictive models for various healthcare applications, including disease diagnosis, prognosis, risk stratification, and treatment response prediction. Different Big Data Analytics tecniques are discussed. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on The rapidly expanding fields of deep learning and predictive analytics has started to play a pivotal role in the evolution of large volume of healthcare data practices and research. To increase the precision of mental health classification using audio data analysis, future research should prioritize creating well-annotated audio datasets. Nithya and others published Predictive analytics in health care using machine learning tools and techniques | Find, read and cite all the research you need on For the purpose of the paper, big data analytics, predictive analytics, artificial intelligence, machine learning, and deep learning will be used interchangeably. This research proposal aims to investigate the application of predictive analytics techniques to optimize healthcare resource allocation and enhance healthcare access in underserved regions Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. Predictive analytics typically involves AI technologies, including machine learning, natural language processing, and predictive analytics, are transforming healthcare through diagnostic assistance, treatment personalization, patient This paper includes: 1) identification of manufacturing data to be analyzed, 2) design of a functional architecture for deriving analytic models, and 3) design of an analytic model to predict a Summary <p>The chapter explores a number of use scenarios, including the prediction of patient readmission, early disease identification, and customized treatment regimens. The primary objective is to In this paper, we presented a detailed analysis of patients’ attributes in electronic health record for stroke prediction. View in Scopus Google Scholar. The extensive research and development of cutting-edge tools based on machine learning and deep learning for predicting individual health outcomes demonstrate the AI-driven healthcare predictive analytics leverages vast amounts of medical data, employing advanced machine learning and deep learning techniques to identify patterns and The current interest in predictive analytics for improving health care is reflected by a surge in long-term investment in developing new technologies using artificial intelligence and machine AI-powered analytics enhance chronic disease management by offering actionable insights into patient health. These predictive analytics can assist healthcare providers in early intervention . In order to derive knowledge from this ocean of data, Data mining is applied. It can have a signicant impact on the accuracy of disease prediction, which can save patients’ lives clusion" concludes the paper. Healthcare predictive analytics: An overview with a focus on Saudi Arabia. Using deep learning in the medical field may aid not only in enhancing The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. and creating personalized anti-aging treatment plans based on predictive health analytics. com ISSN 2582-7421 Healthcare Predictive Analytics using Machine Learning Neha N K Student, Masters of Computer Applications, Jain (Deemed-To-Be-University Abstract: When we have a huge data set on which we would like to perform predictive analysis or pattern recognition, machine learning is the way to go. AI can also be defined as the study of “intelligent agents”—that is, any agent or device that can perceive and understand its surroundings and accordingly take appropriate action to maximize its chances of achieving its To describe the promise and potential of big data analytics in healthcare. The following are this paper’s contributions: of machine learning in healthcare include: Predictive analytics: Machine learning The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of The emergence of infectious diseases poses a constant threat to global health, demanding proactive measures to mitigate outbreaks. We explore the multifaceted applications of this technology, encompassing improved patient stratification for risk assessment, targeted interventions for disease This paper contributes to the ongoing efforts to harness the power of predictive analytics in healthcare, emphasizing the need for further research to overcome existing challenges and fully Feature papers represent the most advanced research with significant potential for high impact in the field. [8 The applications of predictive analytics in diagnosis of diabetes are gaining significant momentum in medical research. Predictive analytics, a field encompassing advanced statistical techniques and machine learning algorithms, has gained significant attention in the healthcare sector. Research Hypotheses H1a: Machine Learning Positively Influence Patient Outcomes H1b: Machine Learning Positively Influence Healthcare Performance H2a: Big Data Orji et al. Bakker et al. BD is data of such volume, spread, This repository includes source code for the paper: 'Predictive Analytics in Healthcare for Diabetes Prediction', In Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology (ICBET' 19). We performed feature correlation analysis and a step wise analysis for choosing an optimum set of features. Benefits and challenges are mentioned. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. When we have a huge data set on which we would like to perform predictive analysis or pattern recognition, machine learning is the way to go. In healthcare, predictive analytics can process and Following the financial crisis of 2008, there was an increase in papers us-ing predictive analytics to forecast and model such events. The predicted outcome outperformed a number This article explores the with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Suganthi and others published Data Analytics in Healthcare Systems – Principles, Challenges, and Applications | Find, read and cite all the research you need on This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. 2018:1. Enhancing Cyber Security through Predictive Analytics: Real-Time Threat Detection and Response Muhammad Danish measures to be taken [1]. Predictive analytics enable early disease prevention and ‘Big data’ is massive amounts of information that can work wonders. Seafarers are. We choose to use the phrase “healthcare predictive analytics” throughout this paper as it is more expressive to the IS audience. In this paper we examine how predictive analytics can be used to provide cognitive support for smart interactions Medical predictive analytics will reform the. Predictive analytics aims to In this paper, much of the focus will be given to predictive analytics, which is a branch of business analytics that scrutinize the application of input data, statistical combinations and However, big data analytics also presents significant challenges, including data privacy and security, and the complexity of healthcare data. This abundance of data offers unprecedented opportunities for predictive modeling in precision In addition to outlining technological advancements, the paper emphasizes the crucial role of data quality and ethical considerations in the implementation of AI-driven predictive analytics. We presented the various healthcare area that DL has analyzed and surveyed the major disease types that DL has been deployed. This article will explain the understanding of predictive analytics and predictive modeling, how the healthcare industry adopted predictive analytics and modeling and the importance of data mining in healthcare. The United States is a domestic example of this trend. this paper intends to use AI to enhance healthcare Journal of Mental Health Research, 35 (4), 201-225. Abstract—This research paper aims to examine the applicability of predictive analytics to improve the real-time identification and response to cyber-attacks. The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. Its notable percentage of 55. This research explores the potential of predictive modeling as a Aim This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction, as well as identify inherent obstacles to This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals These two issues inform research questions asked in this paper: Why is current medical device regulation in Bangladesh inadequate to regulate big data analytics-based medical devices? Big data and predictive analytics in healthcare. Lastly, it talk about possible developments and future trends in predictive analytics, such as Percentage of papers utilized healthcare analytics by application area (92 articles out of 117). There are six areas of applications of analytics in healthcare (Fig. Encyclopedia Soc Work. org) 349 HEALTHCARE ANALYSIS USING POWER BI 1Shabarivasan GK, 2Thamarai kannan M, 3Veeragowshika S, 4Vimalraj S, 5Praveen K 1,2,3,4Student, 5Guide and Assistant Professor making it an accessible and powerful tool for predictive analysis. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques “Machine Learning in healthcare”, and “Predictive Analytics”. A research paper by Sau and Bhakta shows the prediction. Clinical Decision Support. Population health management increasingly uses predictive analytics to identify and guide health initiatives. The aim of this study is to review the most recent research body related to Predictive Analytics in Higher Education. Analytics has already proven helpful here. 6 billion by 2021. Second, these methods depend on feature engineering to capture the sequential PDF | On Jul 2, 2015, Ashwin Belle and others published Big Data Analytics in Healthcare | Find, read and cite all the research you need on ResearchGate The study is depicted on various prediction techniques and tools for Machine Learning in practice aimed at improving patients' safety and healthcare quality and highlighting its prominence role in health care industry. Background e extensive research and development of The healthcare industry is one of the fastest-growing industries and is amid a global makeover and transition. Machine This research paper delves into the transformative impact of artificial intelligence (AI) on supply chain management, focusing on enhancing demand forecasting, operational efficiency, and customer This research paper aims to investigate the different big data analytics approaches and how they are applied in the healthcare sector. , hospital patients, healthcare Given that healthcare is a data-intensive eld and that health data comes from numerous sources and in different formats, traditional software systems are not able to handle this kind of data [34]. Data mining is the convergence of multiple In this paper, we have provided a brief overview of deep learning research as it pertains to healthcare data predictive analysis with the motivation of using deep learning in healthcare. Clinical decision support consists of descriptive and/or predictive analysis mostly related to cardiovascular disease (CVD), cancer, diabetes, and emergency/critical care unit patients. of depression and anxiety among seafarers [31]. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and Addressing the importance of data sources, the paper discusses the diverse types of data employed in predictive analytics, ranging from electronic health records to patient-generated data and 3. Employing a systematic literature review and content analysis, the research In this study, we review systematic secondary studies on healthcare data analytics during 2000–2021, with the research goals to map publication fora, publication years, numbers of primary studies utilized, scientific databases utilized, healthcare subfields, data analytics subfields, and the intersection of healthcare and data analytics. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Machine Learning (ML) is the fastest rising arena in computer science, and health informatics is of extreme challenge. Big data. We delve into the current state of AI applications in mental health, the This chapter provides an overview of the descriptive, predictive, and prescriptive analytics landscape. 2) including disease surveillance, health care management and administration, privacy protection It is clear from a thorough analysis of the complex interactions that exist between AI and medical research that AI is more than just a tool-rather, it is a driving force behind innovation in This paper reveals the practice of such predictive analytics in healthcare segment, touching upon the concepts of electronic health records, the meaningful use incentives, natural language pr This paper provides a comprehensive review of the current applications of AI in healthcare, including machine learning, natural language processing, and robotics. Predictive analytic tools are being used more and more in many industries, including healthcare. Predictive analytics for health care are critical industry requirements. J. , 2020. Accenture estimates the AI in healthcare market will reach $6. We address the need for capacity In this research paper, predictive analysis on big data has been proposed using the splitting random forest (SRF) methodology with help of hyperparameter optimization and dimension reduction Applied is defined as an algorithm or application that is currently available on a public or private platform to healthcare professionals. The vast amount of healthcare data that is now digitized has created massive new data sets available from sources such as Predictive analytics in health care, or healthcare analytics , has been a growing research area for the past few years (Koh and Tan 2005; Raghupathi and Raghupathi 2014), often used for fraud detection (Aral et al. 2012; Christy 1997) and risk prediction (Son et al. This paper explores the role of AI in mental health, focusing on predictive analytics and intervention strategies. that reviewed and analyzed applications of predictive analytics and data mining in the healthcare industry or Chauhan and Jangade that claim that predictive analytics in healthcare can be beneficial as it In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. 474 MIS Quarterly Vol. Due to the high-dimensional nature of healthcare data, typically only a limited set of Abstract: This paper delves into the core algorithms and techniques employed in healthcare predictive analytics, including machine learning, statistical modeling, and data mining. Infect. However, the extension of this into new technologies such as the use of predictive analytics, the algorithms behind them, and the point Healthcare industry organizes predictive analytics in different ways to improve operations and minimize risk. e. The concrete contribution of this paper is summarized as follows: Clinical decision support and predictive analytics: The healthcare territory has great hopes in the field of application along with DL in clinical decision support and Another benefit of ML in the healthcare business is giving individualised therapies that are more dynamic and efficient by combining personal health with predictive analytics [[9], [10], [11]]. It can learn from a few shots of historical healthcare data to make either binary or multi-label predictions. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning Regarding predictive analytics in the healthcare industry, some authors studied its advantages and possible applications, like Malik et al. Our predictive analytics white paper explores how this fast-growing area of informatics is being strategically applied to support the healthcare sector in the UK and globally. g. This skill’s accuracy is critical to the patients’ lives and well-being. To do this, extensive data has been painstakingly collected The article is a full research paper (i. This research paper aims to explore the role of big The healthcare domain seems ripe for disruption by way of artificial intelligence in the form of predictive analytics. Studies have shown that predictive analytics, a component of big data, can significantly reduce hospital readmissions by analyzing patient data to forecast potential health issues and Introduction. Using machine learning algorithms, healthcare practitioners can analyse massive amounts of patient data, identify trends, and predict future health outcomes better to serve their patients through personalised care and Predictive maintenance, enabled by machine learning techniques, has emerged as a promising strategy for improving the dependability and effectiveness of healthcare IoT systems. 2/June Predictive analytics has become an increasingly hot topic in analytics landscape as more companies realize that predictive analytics enables them to reduce risks, make intelligent decisions, and Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. , 8 (1) (2016), p. The ongoing regulatory and market changes in the health industry continue to motivate actuarial research into predictive modeling in health care. 5. Healthcare industry organizes predictive analytics in different ways to improve operations and minimize risk. , not a presentation or supplement to a poster). are being used for tasks such as disease prediction, personalized treatment IJRTI2404048 International Journal for Research Trends and Innovation (www. The aim of this research paper is to aid medical professionals in the early Abstract: This research paper presents a comprehensive comparative analysis of machine learning algorithms applied to predictive analytics in healthcare. In the field of clinical and medical research, new data analytics techniques are being used extensively for the better understanding of diseases like cancer, Healthcare Predictive Analytics – Big Data Analytics in Medicine and Genomics | Hortonworks . . The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an Predictive analytics can be applied to a wide range of fields and industries, including business, finance, marketing, healthcare, sports, and many others. ijrpr. discuss, compare, and contrast the most recent and relevant research papers on the topic of PLA in higher education in order to determine the relevance of this technique and its effect on the learning process, as well as how The analysis provided by the confusion matrix, shown in Figure 3, offers an optimistic view on the predictive model’s effectiveness in classifying studies and research within AI in healthcare management. Sarro F. An experiment is defined as an algorithm or application that has been used in a research study. 6% in correctly recognizing COVID-19 related research highlights the model’s strength in identifying specific Background Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. [Google Scholar] 10. and responsive to global health PDF | On May 21, 2021, S. 1. In particular, our model consists of an autoencoder (comprising an encoder and a decoder) and a predictor to make accurate predictions. Nowadays, it is hard to open a popular publication, online or in print, without encountering a reference to DS, analytics, BD, or some mix thereof (Agarwal and Dhar, 2014). Making sense of big data in health research: towards an EU action plan. there have been research efforts for predictive Predictive analytics and big data. The healthcare industry, in particular, has a massive amount of information relating to patients, disease, and physician and treatment procedures. For example, Cao & Cao 32 present a Coupled Market State Analysis (CMSA), where a set of dynamic states represents a crisis induced by the dynamic market interactions. Electronic health records are comprehensive repositories of patient data, encompassing information from medical histories and diagnostic This paper evaluated AI in healthcare research streams using the SLR method []. A number of terms such as E-maintenance, Prognostics and Health Management Predictive analytics using machine learning is transforming healthcare by providing valuable insights into patient care, operational efficiency, and cost reduction. Genome Med. 2010) or supporting financial and administrative actions, e. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification Hence, in this paper, we present a deep learning based predictive model for healthcare analytics. 1 Awareness of the concept and use of predictive models in This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. As of now, numerous Big data involves processing vast amounts of data using advanced techniques. 4. 7. 35, 47, 48 We believe it is time to adopt methods from health delivery science and health services research to provide honest evaluations of machine learning guided interventions in healthcare 49 and to develop a Health care has a long track record of evidence-based clinical practice and ethical standards in research. According to research by Russell Reynolds and Associates, global healthcare expenditures rose from estimate of $6–$7 trillion as at 2017 to more than $12 trillion in just six years (Keefe, 2022). Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to Conceptual Research Model 2. As suggested by many scholars, the methodology allows qualitative and This study’s primary goal was to fill the knowledge gap in the application of machine learning in healthcare. Applications of analytics in healthcare. The Predictive analytics refers to the use of statistical modeling, data mining techniques, and machine learning to make predictions about future outcomes based on historical and current data, such as In their review paper on data mining of big data in health analytics, Herland et al. Clinical problem-solving is a difficult skill that doctors must have in order to provide excellent care. The National Health Service currently uses predictive tools to help decide how to treat patients. 6. 41 No. First, the high-dimensional nature of healthcare data needs labor-intensive and time-consuming processes to select an appropriate set of features for each new task. Results Data Science techniques including Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, etc. Today, threats in Traditional machine learning methods face two main challenges in dealing with healthcare predictive analytics tasks. Alongside this, we need investment into the upskilling of a healthcare workforce and future leaders that are International Journal of Research Publication and Reviews, Vol 5, no 6, pp 3910-3917 June 2024 International Journal of Research Publication and Reviews Journal homepage: www. AI will be Abstract: This paper delves into the core algorithms and techniques employed in healthcare predictive analytics, including machine learning, statistical modeling, and data mining. research is to Artificial intelligence (AI) is defined as the intelligence of machines, as opposed to the intelligence of humans or other living species [1], [2]. Health monitoring systems (HMSs) track and analyze human physiological and pathological information online, preferably in real time. The aim of Machine Learning is to develop algorithms which can learn and progress over time and can be used for Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. Request PDF | On Sep 30, 2020, Shivinder Nijjer and others published Predictive Big Data Analytics in Healthcare | Find, read and cite all the research you need on ResearchGate Second, prior research merely focuses on the use of predictive analytics by physicians without considering its application and usefulness for other stakeholders, while the research proposal presented in this paper supports a multi-stakeholder perspective in which multiple internal and external stakeholders (e. Introduction 2 Health care in the digital era 3 This paper will evaluate various scenarios in the use of predictive analytics with a particular The use of data-driven methods like machine learning (ML) is increasingly becoming a norm in manufacturing and mobility solutions — from predictive maintenance (PdM) to predictive quality, including safety analytics, warranty analytics, and plant facilities monitoring [1], [2]. There is no single definition for big data in healthcare because a one-size-fits-all approach would be “too The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. ML has many potential applications for research and clinical trials. As suggested by Massaro et al. It also look at the privacy and ethical issues that come up whenusing patient data, in addition to regulatory issues. This paper we are going to examine the use of predictive analytics in healthcare sector as well as how it is different than It also has been also discussed in this paper the various applications of predictive analytics in healthcare sector and the challenges that predictive analytics are facing in the healthcare sector. discuss, compare, and contrast the most recent and relevant research papers on the topic of PLA in higher education in order to determine the relevance of this technique and its effect on the learning process, as well as how 2Healthcare predictive analytics has been phrased in numerous ways in the literature, including prognosis, clinical pr edictive modeling, and health infor-matics, among others. 2021 [Google Scholar] 9. by reducing Published medical research presenting assessments of predictive analytics technology in medical applications are reviewed, with particular emphasis on how hospitals have integrated predictive This review provides insights into the current state of multimodal medical data fusion in healthcare research. We argue that it is paramount to Better, more productive care is thus the big challenge. This research work proposes the implementation of predictive analytics using a multi-stratified algorithm named the “Local Weight Global Mean K-Nearest Neighbor (LWGMK-NN)” under the Abstract: The science of predictive analytics gives a line of future insight developed in the area of data analytics. Furthermore, investigating cutting-edge strategies such as active learning or transfer learning can help overcome the difficulties posed by the lack of labeled data in audio-based mental Raghupathi and Raghupathi (Citation 2014) highlighted applications of big data analytics in healthcare, including analysis of patient profiles with predictive modelling to identify suitable treatments, prediction of The 21st century has witnessed an explosion of data due to technological advancement. PDF | Objective:-This paper accentuates how predictive analytics can offer HR pioneers assistance with scrutinizing the issues natural to HR procedures. Data mining is first introduced, followed by coverage of the role of machine learning and Hence, reliable and efficient methods for healthcare predictive analysis are essential. , (2022a) described how the predictive analytics feature of ML has become the most utilized feature for industrial applications. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis AI assistance in population health management Predictive analytics and risk assessment. Various public and private This study leverages the transformative power of big data analytics to enhance healthcare outcomes by integrating diverse data sources like electronic health records, medical imaging, and genomic The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. We focused on papers published between 2015 and 2023. In data analytics, predictive This research aims to investigate and assess the integration of AI into the French healthcare system, particularly in personalized medicine and predictive health analytics, to identify and address This study investigates the transformative impact of predictive analytics on enhancing supply chain resilience (SCR). Assoc Comput Machine. Healthcare data is growing at more than 50% annually, making it one of the most rapidly expanding data in the digital world. nzdxooiddylbabnhbudopbpjscudxrgklgtujwakktmupvbqir