IoT-enabled biosensors for real-time monitoring and early detection of chronic diseases.

In numerous perilous cases, a quick medical decision is needed for the early detection of chronic diseases to avoid austere consequences that may be fatal. Chronic kidney disease (CKD) is a prevalent disease that presents a variety of challenges, including soaring costs for intervention, urgency, and, more importantly, difficulty in early detection of the disease. The current study carries out a prediction-based method that helps in detecting and diagnosing CKD patients which enables a fast and accurate decision-making process at the early stage. A combination of preprocessing and feature selection methods was developed; additionally, several prediction models, such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and bagging, were trained based on the processed dataset. The performance evaluation shows higher reliability of all models in terms of accuracy, precision, sensitivity, F-measure, specificity, and area under the curve (AUC) score. Specifically, KNN outperformed with an accuracy of 99.50%, sensitivity of 99.2%, precision of 100%, specificity of 98.7%, and F-measure and AUC score of 99.6%. The experimental results of KNN show the best fitted model compared to the existing state-of-the-art methods. Moreover, the reduced feature set proves that just a few clinical tests are enough to detect CKD, resulting in diagnosis cost reduction.

The rapid advancement of predictive modeling techniques has revolutionized early detection and management of chronic diseases. This chapter explores the integration of multi-modal health data and the application of advanced computational methods to enhance predictive accuracy in chronic disease prediction. By leveraging diverse data sources, including EHRs, wearable devices, genomics, and imaging, multi-modal models offer a comprehensive understanding of patient health, enabling early identification of disease risk. Emphasis was placed on the use of machine learning, graph-based models, and probabilistic approaches to capture complex interdependencies within heterogeneous data streams. Challenges related to data preprocessing, semantic interoperability, and bias mitigation in predictive systems are critically examined. The chapter also highlights the role of explainability in ensuring transparency and fairness, ensuring that predictive models are both clinically effective and ethically sound. Future directions for the integration of cutting-edge technologies such as federated learning and edge computing are also discussed, alongside their potential to transform population health monitoring and chronic disease management.

Integrating Internet of Things (IoT) with Artificial Intelligence (AI) is one of the catalysts for improving traditional healthcare services. This integration has created many opportunities that have led to healthcare shifting towards enabling home care, the concept that harnesses the technologies advanced potential such as the IoT and deep learning for intelligent monitor and manage chronic diseases. As population growth increases, restrictions on traditional healthcare services increase. Some diseases, such as chronic diseases, require innovative solutions that go beyond the boundaries of traditional healthcare settings due to their impact on individuals’ health for example traditional healthcare systems have little capacity to provide high-quality and real-time services. Empowering home care services using deep learning and internet of things technology is promising. It enables continuous monitoring through interconnected devices and deep learning, which provides intelligent insights from massive data sets. This brief explores the key components of enabling home care, including continuous patient health monitoring, predictive analytics, medication management, and remote patient support by healthcare providers, and provides friendly interfaces for end-users. The conjunction between the IoT and deep learning in home-care signals a shift toward precision medicine, enhancing patient outcomes and creating a sustainable model for chronic disease management in the era of decentralized healthcare. This review article aims to discuss the following aspects: presenting the latest technologies in home care systems, showing the merit of combining the Internet of Medical Things (IoMT) and deep learning and its role in monitoring patient conditions and managing chronic disease to improve patient health status accurately, in real-time, and cost-effective, and lastly, debating future studies and providing recommendations for the ongoing development of home care remote monitoring applications.

Particular attention is paid to preventive work within the framework of the implementation of the national projects «Healthcare» and «Demography», initiated by the President of Russia Vladimir Putin. In the region, with the support of the Governor of the Samara Region, active work is being carried out to strengthen the healthcare system, including the development of preventive medicine. Medical examinations are carried out for the early detection of chronic non-communicable diseases and risk factors for their development. Risk factors include: high blood pressure, sugar and glucose in the blood; smoking; alcohol and psychoactive substance use; poor nutrition; low physical activity; overweight. Objective. To increase the coverage of medical examinations and preventive medical examinations of working-age individuals in work teams at an industrial enterprise, to organize 100 % referral to the second stage of medical examination of patients with suspected chronic non-communicable diseases. Results. Increase in the detection of pathology within the framework of medical examinations an 100 % registration of patients with newly diagnosed pathology for medical examination. Conclusion. Medical examinations and medical examinations are the basis of prevention, they help in the early detection of chronic diseases that can lead to premature mortality and disability. A timely and correct diagnosis will allow the necessary treatment to begin. The pilot project made it possible to save time significantly and undergo examinations without interrupting work.

Artificial intelligence (AI) has become a revolutionary agent in healthcare, specifically for the early detection, surveillance, and treatment of chronic diseases. Across a range of applications, AI facilitates multimodal diagnostic systems, conversational virtual assistants, wearable integration, and blockchain-enabled data security, to improve both precision medicine and individualized care. Research emphasizes its use in rural and disadvantaged communities through enhanced access, decreased diagnostic delays, and management of diseases like diabetes, cardiovascular conditions, respiratory conditions, and neurodegenerative diseases. Virtual health companions and artificial intelligence-based telemedicine interfaces enable triage, symptom monitoring, and patient interaction, with smart wearable technology offering ongoing physiological monitoring, leading to enhanced predictive analytics for disease progression. Systematic reviews verify that AI not only enhances accuracy and efficiency in clinical decision-making but also aids in long-term care through centralized monitoring systems and smart health coaching. Challenges still exist in interoperability, data privacy, and ethics despite breakthroughs. Future directions highlight hybrid models that combine AI with blockchain, the Metaverse, and next-generation telemedicine to enhance chronic disease management worldwide. Together, these advancements represent a shift paradigm toward accessible, data-informed, and patient-focused healthcare solutions, with promising avenues for early intervention and sustainable care delivery.

BackgroundThe Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns.MethodsThe cross-sectional study involved healthy Ghanaian participants (n = 217; aged 30–80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations.ResultsWe observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks (p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions.ConclusionsWe have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage.

Chronic diseases such as diabetes, cardiovascular conditions, and cancer represent a growing global health burden, contributing significantly to morbidity, mortality, and rising healthcare costs. Traditional diagnostic methods often fail to identify early indicators, leading to delayed interventions and reduced treatment effectiveness. This paper proposes a conceptual framework for the early detection of chronic diseases using Artificial Intelligence (AI) and predictive analytics. The framework leverages machine learning algorithms, electronic health records (EHRs), wearable device data, and real-time health monitoring systems to identify high-risk individuals and predict disease onset before clinical symptoms appear. The conceptual framework integrates four key components: data acquisition, data preprocessing, model development, and decision support. Data acquisition encompasses structured and unstructured data from diverse sources, including clinical records, genetic profiles, lifestyle information, and sensor-based health monitoring. Preprocessing involves cleaning, normalization, and feature selection to enhance data quality. Advanced AI models, particularly deep learning and ensemble methods, are trained on historical datasets to uncover patterns, correlations, and risk factors. The decision support layer translates predictive outcomes into actionable insights for healthcare providers, enabling timely and personalized interventions. The framework emphasizes interoperability, scalability, and privacy preservation, ensuring secure and efficient data sharing across healthcare ecosystems. It also highlights ethical considerations, including algorithmic transparency, bias mitigation, and informed consent. Implementation of this framework can transform chronic disease management by shifting the focus from reactive treatment to proactive prevention. This approach can reduce hospitalization rates, improve patient outcomes, and optimize resource allocation in healthcare systems. The proposed framework serves as a strategic guide for healthcare stakeholders, policymakers, and researchers aiming to harness AI for sustainable public health improvement. It underscores the transformative potential of integrating predictive analytics into early detection protocols, paving the way for smarter, data-driven healthcare delivery. Future research will focus on clinical validation, model optimization, and integration into existing healthcare infrastructure.

BackgroundChronic disease is the leading cause of morbidity and mortality among Aboriginal and Torres Strait Islander peoples in Australia. A comprehensive health assessment is available as an annual health check (HC) to Aboriginal and Torres Strait Islander peoples through the Medicare Benefits Schedule in primary health care settings. This review aims to systematically identify contextual and mechanistic factors that contribute to the success or failure of implementing effective HCs in the prevention and early detection of chronic diseases among Aboriginal and Torres Strait Islander people in Australian primary health care (PHC).MethodsWe systematically searched for peer-reviewed and grey literature, including policy reports, theses, and guidelines, between 1 November 1999 and 30 June 2023, using a combination of keywords and subject headings related to “health checks”, “chronic disease”, and “Aboriginal and Torres Islander peoples” in seven databases. The extracted data were summarized using a content analysis approach, applying strength-based approaches.ResultsIn total, 16 peer-reviewed articles and five grey literature that met the inclusion criteria were used for evidence synthesis that identified several contextual and mechanistic factors that influenced the implementation of HCs. Barriers included resource constraints driven by complexities in administrative, workforce and policy domains that significantly impeded the implementation of HCs. Within PHC, physical space constraints, competing demands and a focus on acute care over preventive measures hindered HC implementation. In addition, inconsistent identification of Aboriginal and Torres Strait Islander status, negative attitudes of PHC staff towards HC efficacy and patients’ fear of stigma or confidentiality breaches were barriers. Patients reported HCs as failing to address holistic health needs. To improve HC implementation, enablers included strong clinical leadership, recruitment of culturally competent non-Indigenous and Aboriginal and Torres Strait Islander staff, Indigenous partnership and community engagement and incentives for participation. Effective electronic records, transport provision and flexible scheduling also increased accessibility.ConclusionsOur findings suggest that future implementation research must adopt a more comprehensive and holistic approach across different models of PHC, with clearly identified contextual and mechanistic factors linked to people-reported and service outcomes, to guide the implementation and evaluation of HCs. While undertaking future research, it is crucial to implement policy and practice reforms as identified in this review to create a culturally safe service at the PHC level required to drive the uptake of quality HCs that aligns with community priorities and aspirations for the prevention and early detection of chronic diseases.

Michigan’s CHRONICLE, the Chronic Disease Registry Linking Electronic Health Record Data, is a near–real-time disease monitoring system designed to harness electronic health record (EHR) data and existing health information exchange (HIE) infrastructure for transformative public health surveillance. Strong evidence indicates that using EHR data in chronic disease monitoring will provide rapid insight over time on health care use, outcomes, and public health interventions. We examined the potential of EHR data for chronic disease surveillance through close collaboration with our statewide HIE network and 2 participating health systems. We describe the development of CHRONICLE, the promising findings from its implementation, the identified challenges, and how those challenges will inform the next steps in testing, refining, and expanding the system. By detailing our approach to developing CHRONICLE and the considerations and early steps required to build an innovative, EHR-based chronic disease registry, we aim to inform public health leaders and professionals on the value of EHR data for chronic disease surveillance. With systematic testing, evaluation, and enhancement, our goal for CHRONICLE, as a fully realized and comprehensive surveillance system, is to model how collaborative health information exchange can support evidence-based strategies, resource allocation, and precision in disease monitoring.

Introduction: The older people with chronic diseases are one of the populations that have a susceptibility to COVID-19. The community-based program that help older people manage their lifestyle was postponed due to avoiding crowds and physical contacts. This literature review aims to describe a mobile-app for the older people with chronic diseases and its benefits. This literature review aims to describe a mobile-app for the older people with chronic diseases and its benefits.
 Method: The method used in this study is a literature review. This study is analyzed by selecting some literature that is relevant to the purpose of writing in order to obtain new conclusions. Online databases used are ProQuest, Pubmed and Science Direct starting from 2019 to 2021. The keywords used to sort articles in this study are: 1) older people, 2) mobile app, 3) COVID-19.
 Results: There was ten selected articles that analyzed. This literature review resulted in three main themes in the use of mobile-apps for the older people with chronic diseases, namely (1) monitoring of chronic diseases and COVID-19, (2) management of chronic diseases and COVID-19, and (3) the perspective of older people caregivers. The implication for nursing services is that nurses can disseminate information and provide interventions by minimizing physical contact. In addition, these results also have implications for the scope of nursing education, namely adding reference material for studies in health information systems courses.
 Conclusion: These results can be used as a basis for creating mobile-app-based educational media, monitoring, and self-management of chronic diseases for the older people and their caregivers.

Early detection of chronic diseases is essential for reducing long-term health complications and improving patient survival outcomes. Traditional diagnostic systems rely heavily on single-modality data, such as medical imaging or clinical records, which often fail to capture the multidimensional nature of chronic disease progression. This research presents a multimodal deep learning framework that integrates medical images with Electronic Health Records (EHR) to enhance early disease prediction. The proposed system utilizes a Convolutional Neural Network (CNN) for extracting structural and morphological patterns from imaging modalities such as MRI, CT, X-ray, and retinal fundus images. In parallel, an LSTM/Transformer-based encoder processes EHR variables, including laboratory values, comorbidities, vitals, and demographic information. The latent representations from both modalities are fused using an intermediate multimodal fusion strategy to generate a unified patient-level diagnostic prediction. Experimental results show that the proposed multimodal model significantly outperforms image-only and EHR-only models, achieving an overall accuracy of 92.8%, an F1-score of 91.0%, and an AUC of 0.96. Per-class analysis demonstrates substantial improvement in detecting early-stage conditions such as diabetic retinopathy, chronic kidney disease, cardiovascular diseases, and COPD. The inclusion of Grad-CAM and SHAP-based interpretability analyses further enhances the clinical reliability of the model. Overall, the findings confirm that integrating imaging and EHR data through multimodal deep learning provides a more comprehensive and accurate approach for early chronic disease detection and has strong potential for real clinical implementation.

Chronic diseases such as diabetes, cardiovascular conditions, cancer, and chronic kidney disease are the leading causes of death and disability worldwide. Despite their often-slow progression, these conditions are typically diagnosed at later stages, reducing the effectiveness of interventions and increasing healthcare costs. The rise of predictive analytics, powered by artificial intelligence (AI), provides an opportunity to change this narrative. By leveraging vast and complex datasets including electronic health records (EHRs), genomics, lifestyle data, and real-time biosensor inputs, AI models can identify subtle patterns indicative of disease onset long before clinical symptoms emerge. This study proposes and evaluates a comprehensive predictive analytics framework for the early detection of chronic diseases using multiple machine learning (ML) algorithms. Results from experimental evaluation using diverse, real-world datasets indicate that AI-based models offer high accuracy and interpretability, with some models predicting disease risk years in advance. The implementation of such systems in clinical workflows promises to shift healthcare paradigms from reactive treatment to proactive prevention, enabling personalized interventions that can save lives and reduce economic burden.

Biochemical markers play a pivotal role in the early detection and management of chronic diseases, offering clinicians valuable insights into disease onset, progression, and therapeutic response. This review highlights key biochemical markers commonly used in the diagnosis of cardiovascular diseases, diabetes, liver and kidney disorders, and various types of cancer. The review discusses the long and short-term risk factors for various diseases, disease progression, and prevention. Also, show the extent of the individual's response to treatment, positively or negatively, and the probability of the disease recurrence and progression. Biochemical markers help identify early symptoms and signs by providing a biochemical assessment of various physiological disorders. This review aims at an integrated assessment of the importance of biomarkers in the early detection of chronic diseases by collecting information and evidence and identifying their trend. This review also attempts to demonstrate the diagnostic utility of biomarkers, their clinical applications, and their ability to enhance patient outcomes and improve their response to treatment.

This study aims to considering the assessment of the usability and user experience of applications based on artificial intelligence (AI) for early detection of chronic diseases. Health data is recorded and chronic disease risk is classified using an AI-based early detection of chronic disease application. The study was conducted in Semarang city/regency health service facilities, using quantitative research methodology. The study's inclusion criteria were individuals with a history of diabetes mellitus and hypertension. Using the System Usability Scale (SUS) and User Experience Question (UEQ) surveys, 131 respondents were studied in May–July 2023. The study's findings showed that respondents who were older than 60, female, had not completed their education, worked for a living or were self-employed, did not use a mobile phone, and had never used health applications scored poorly for usability and user experience. The system satisfation aspect receives the lowest grade in terms of system usability, while the memorability aspect has the best score. The efficiency aspect of the system receives the greatest score in terms of user experience, while the novelty aspect receives the lowest. It is known that the AI-based early detection of chronic disease application has a reasonably acceptable usability and user experience based on the findings of the SUS and UEQ questionnaires for patients at risk of hypertension and diabetes mellitus. It is necessary to design an AI-based chronic disease detection application that is easier to learn and more innovative so that it can be used by the wider community.

The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet of Things (IoT) and artificial intelligence (AI) technologies offers a potential resolution by facilitating more precise chronic disease management through data-driven strategies. This review concentrates on the utilization of IoT mobile sensing devices in managing major chronic diseases such as cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes. It scrutinizes their efficacy in disease diagnosis and management when integrated with machine learning algorithms, such as ANN, SVM, RF, and deep learning models. Through an exhaustive literature review, this study dissects how these technologies aid in risk assessment, personalized treatment planning, and disease management. This research addresses a gap in the existing literature concerning the application of IoT and AI technologies in the management of specific chronic diseases. It particularly demonstrates methodological novelty by introducing advanced models based on deep learning, tight frame-based methodologies and real-time monitoring systems. This review employs a rigorous examination method, which includes systematically searching relevant databases, filtering literature that meets specific inclusion and exclusion criteria, and adopting quality assessment tools to ensure the rigor of selected studies. This study identifies potential biases and weaknesses related to data collection, algorithm selection, and user interaction. The research demonstrates that platforms integrating IoT and machine learning algorithms for chronic disease monitoring and management are not only technically viable but also yield substantial economic and social advantages in real-world applications. Future studies could investigate the use of quantum computing for processing vast medical datasets and novel techniques that merge biosensors with nanotechnology for drug delivery and disease surveillance. Furthermore, this paper examines recent progress in medical image reconstruction, emphasizing tight frame-based methodologies. We discuss the principles, benefits, and constraints of these methods, assessing their efficacy across diverse application contexts.