Jul 2026· Recent Advances in Computer Science and Communications· Vol 19· 0 citations
TL;DR
AI-based predictive modeling, in conjunction with healthcare communication systems, can enhance risk management and clinical decision-making to a large extent, and Neural Networks were identified to be the most accurate predictor of chronic diseases.
Abstract
The combination of Artificial Intelligence (AI) and data analytics is
used to transform the sphere of healthcare by moving away from the approach of reactive risk
management to proactive risk management. Predictive models have the potential to detect risks
earlier, maximize interventions, and enhance clinical outcomes with the increasing access to
Electronic Health Records, EHRs, and wearable devices (WD), and real-time patient monitoring.
Nevertheless, issues like data privacy, algorithmic bias, and interoperability continue to be
a serious obstacle to adoption.
This paper explores the creation and use of computational models and machine learning algorithms
of Logistic Regression, Random Forest, and Neural Networks as predictive healthcare
analytics. The objective is to assess their usefulness in predicting chronic diseases, reducing
costs, and early intervention, and to address ethical and technical issues.
Heterogeneous data, such as EHRs, wearable sensors, and medical imaging, were
preprocessed by standard cleaning and feature engineering methods and anonymization. Crossvalidation
and performance measures, including accuracy, sensitivity, and specificity, were
used to predict the development and validation of the predictive models. A comparative study
of Logistic Regression, Random Forest, and Neural Networks was conducted to evaluate predictive
performance. The concept of deployment was taken into consideration concerning
healthcare IoT and communication systems.
Neural Networks were identified to be the most accurate predictor of chronic diseases
(92%), compared to Random Forest (90%) and Logistic Regression (85%). The application of
AI-based predictive analytics has led to a decline in hospital readmission rates by 25 percent,
patient care expenses by 18 percent, and a 40 percent rise in the rate of early interventions. Optimization
of resources also led to a decrease in the average hospital stay by 22.6 and the minimization
of medication errors by 66.7. These findings indicate the huge potential of AI in improving
healthcare outcomes and efficiency.
However, the findings suggest that AI and predictive analytics are used to change
the potential to shift the healthcare model with proactive care. Successful implementation requires
addressing challenges like data interoperability, algorithmic bias, and ethical governance.
Neural Networks offer high accuracy but lack interpretability, while Logistic Regression
(LR) and Random Forest (RF) strike a balance between interpretability and effectiveness. The
paper highlights the significance of hybrid solutions between explainability and performance to
be effectively deployed into clinical practice.
Artificial intelligence and data analytics have the power to make healthcare a proactive
and patient-centered ecosystem, which facilitates early diagnosis, saves money, and
streamlines resource distribution. Logistic Regression is interpretable, Random Forest is robust,
and Neural Networks are maximally accurate. Although the results are promising, to
achieve successful adoption, it is necessary to resolve the problem of data privacy, interoperability,
and algorithmic fairness. This paper shows that AI-based predictive modeling, in conjunction
with healthcare communication systems, can enhance risk management and clinical
decision-making to a large extent.
Predictive analytics in healthcare has revolutionized medical decision-making by enabling early disease detection, risk stratification, and personalized treatment plans. However, the implementation of predictive analytics relies on robust data engineering processes to handle the vast amounts of structured and unstructured healthcare data. The integration of electronic health records (EHRs), genomic data, and real-time patient monitoring systems presents significant challenges related to data quality, interoperability, security, and computational efficiency. This paper explores the critical role of data engineering in predictive analytics, addressing key challenges such as data acquisition, cleaning, storage, and real-time processing. Furthermore, it discusses various solutions, including data integration frameworks, cloud-based infrastructures, and artificial intelligence (AI)-driven data processing techniques. The research highlights emerging trends such as federated learning, blockchain for data security, and automated data pipelines that enhance the scalability and accuracy of predictive models. The paper concludes by emphasizing the need for standardized data governance policies, cross-institutional collaborations, and advanced machine learning algorithms to overcome data engineering challenges and improve healthcare outcomes.
Sophia White· International Journal of Art...· 0 citations
Smart healthcare systems can be viewed as a paradigm shift in the contemporary medical practice, since modern placement of sensing technologies, electronic health records (EHRs), and predictive machine learning (ML) technologies have become a common practice that facilitates proactive, personal, and efficient healthcare delivery. Such trends as the growing rate of chronic illnesses, the aging trend, and escalating healthcare costs have led to a desperate demand of smart systems able to diagnose and predict risks early, as well as make the optimal clinical decision. Predictive machine learning predictive models are models based on historical and real-time healthcare data that reveal the latent patterns, predict the disease progress, and assist clinicians to make evidence-based decisions. In this paper, predictive machine-learning has been adopted to analyze in detail the concept of smart healthcare systems, which involve system architecture, data acquisition, feature engineering, model development, and performance evaluation. The suggested framework combines the wearable sensor data, clinical records, and demographic information into a single analytics pipeline. Some of the machine learning algorithms under analysis and supervised or unsupervised, like logistic regression, support vector machines, random forests, gradient boosting, and deep neural networks, are discussed in the context of healthcare prediction tasks, such as disease risk prediction and hospital readmission prediction and patient outcome prediction. Moreover, the paper also touches on the issues associated with data quality, privacy, security, model interpretability and ethical issues. Recent experimental findings indicate that predictive ml-based healthcare systems are much better than conventional rule-based healthcare systems in terms of prediction accuracy and decision support. The results also show the promise of predictive machine learning to turn healthcare into either a reactive treatment or a preventive, personalized healthcare, as part of the vision of smart and sustainable healthcare ecosystems.
Chinedu Okafor· International Journal of Art...· 0 citations
Future research focuses on integrating Internet of Medical Things (IoMT) devices, real-time monitoring, and federated learning to enable privacy-preserving collaboration across healthcare institutions.
R. T· International Journal of App...· 0 citations
Relevance
. The modern healthcare system is entering a stage of deep digital transformation, with artificial intelligence becoming a key tool. The development of algorithms, the growth of computing power and the accumulation of large amounts of medical information have made it possible to move from theoretical models to real digital solutions in clinical and management practice. From a managerial point of view, artificial intelligence opens up new opportunities for analyzing big data, predicting the needs of the population for medical care, optimizing patient routing and resource allocation. The use of intelligent analytical platforms helps to increase the efficiency of medical organizations, reduce the administrative burden and implement the principles of quality management. From a clinical perspective, artificial intelligence is becoming a tool for improving the accuracy of diagnosis, early detection of diseases, personalization of treatment and evaluation of the effectiveness of therapy. Machine learning algorithms are actively used in radiology, cardiology, oncology and telemedicine, providing an additional level of expert support to the doctor and reducing the likelihood of diagnostic errors. Special attention is paid to issues of ethics, clinical verification of results, protection of personal data and preservation of the leading role of the doctor as a responsible decision-making subject. The article examines the organizational, professional and socio-cultural aspects of the use of artificial intelligence in healthcare in the Republic of Uzbekistan.
The purpose of the study
: to summarize modern approaches to the use of artificial intelligence in healthcare, evaluate the clinical and managerial possibilities of its use, identify limitations and ethical aspects.
Materials and methods
. An analytical review of international publications (WHO, NEJM, JAMA, Nature Medicine, and others), as well as regulatory documents on digital healthcare, was conducted. A descriptive and analytical method was used to summarize data on clinical and managerial applications of artificial intelligence.
Results
. The use of artificial intelligence is highly effective in the following areas: medical image analysis (computer and magnetic resonance imaging, X-ray); disease prediction (cardiovascular, oncological, endocrine); clinical decision support; monitoring of chronic patients; patient flow management; optimization of healthcare resources. In a management context, artificial intelligence makes it possible to analyze large amounts of data, predict the burden on the healthcare system, and increase the efficiency of resource allocation. Despite its high productivity, its implementation is accompanied by a number of limitations. The main problems are the quality of the source data, the risk of algorithmic errors, and limited interpretability of the models. Ethical issues are of particular importance: the protection of personal data, the transparency of algorithms and the preservation of the role of the doctor as a subject of decision-making. Artificial intelligence does not replace clinical thinking, but acts as a tool to enhance it. Thus, a new healthcare model based on the use of digital technologies is being formed.
Conclusion
. Artificial intelligence is an important tool for healthcare transformation, improving the quality of medical care and management efficiency. Its application requires the development of digital infrastructure, the training of medical personnel and the formation of a new ethical and legal model.
Hypertension is one of the Crucial global contributors to cardiovascular diseases and death. It often progresses without any significant symptoms. Conventional care models are bounded by intermittent clinical measurements, therapeutic inertia, and inadequate personalization. Recent advancements in Artificial Intelligence (AI) empower a shift toward proactive, precision‐driven hypertension management. It keeps inspection, early risk detection, and intelligent clinical decision support. This systematic review analyses the effectiveness of integrated AI‐based hypertension prediction and management systems those integrate real‐time oversight, interpretable risk prediction, customized lifestyle intervention, clinical decision support, and early warning procedure. Following the PRISMA 2020 guidelines, 40 peer‐reviewed studies those were published between 2020 and 2026 were systematically reviewed across prominent healthcare and AI databases. AI‐driven systems validated strong predictive performance like high AI accuracy of Area Under the Curve (AUC) up to 0.97 and improved early detection and clinical actionability, but results are hard to compare due to differences in data and methods. Integrated AI‐based systems demonstrate strong potential to transform hypertension care, though long‐term validation remains limited.
The integration of AI technology within the health sector has brought about a technological revolution through intelligent data analytics, intelligent decision-making, and intelligent monitoring of patients. There has been an increased amount of data generated due to the integration of electronic medical records, wearable devices, biomedical sensors, and the Internet of Medical Things (IoMT). Intelligent healthcare systems that make use of AI technology can continuously capture data about patients and provide intelligent analytics on the state of their health.
This theoretical research work examines the importance of the application of AI technology in health care with regard to intelligent monitoring of patients through AI technology. The utilization of AI systems for patient monitoring offers various benefits, including constant observation, disease prediction, personalized healthcare suggestions, clinical workload reduction, and patient safety. Such systems may be applied in critical care monitoring, chronic disease management, remote healthcare services, and preventive healthcare. Nevertheless, there exist some barriers to implementation, such as the protection of patients' data, cyber threats, ethical issues, explainability of AI models, poor data quality, and regulation.
The research contributes to a better understanding of AI-based patient monitoring systems and emphasises the importance of implementing such systems for creating future healthcare environments. The results of the research show that the combination of artificial intelligence and healthcare technology allows the provision of efficient, predictive, and patient-centered medical services. Further improvements of explainable AI systems, data management, and personalized intelligent healthcare are expected in the future.
K. Gomathi, Medhunhashini D. R.· International Journal of Cre...· 0 citations
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