Large Volumes Of Medical Data Research Articles

Research on the Application of Machine Learning in Cancer Prediction and Diagnosis

Due to the continuous development and advancement of artificial intelligence technology, the application of machine learning in the medical field, particularly in cancer treatment and prediction, is becoming increasingly widespread and profound. By utilizing machine learning algorithms, healthcare professionals can analyze large volumes of medical data more accurately through specific models. This paper discusses the application of machine learning in cancer diagnosis and treatment, with special attention to prediction, diagnosis and treatment. By analyzing a large number of clinical and genomic data, machine learning, which provides support for early diagnosis, reveals the complex pattern of cancer development. Deep learning technology can help doctors diagnose and predict cancer more accurately in medical image analysis and genome data analysis. Ensemble learning methods such as the random forest model improve the accuracy of prediction. It also introduces semi supervised learning, which provides a new perspective for cancer prediction: enhancing model training by using unlabeled data. The paper also introduces the application of cox-net and survival support vector machine in survival analysis.

IDMap: Leveraging AI and Data Technologies for Early Cancer Detection

Cancer screening is vital in cutting mortality rates, and containing the impact of cancer in a worldwide basis. The current conventional detection techniques including imaging and biopsy though efficient are also characterized with drawbacks like; invasive, expensive, and inaccurate. This abstract will describe the new AI and data solution in the fight against early cancer detection, which presents a massive opportunity to improve accuracy, cut down the time that it takes to deliver a diagnosis, and bring quality health care to possibly millions of patients. ML and, in particular, DL are prospective in terms of decision making upon medical data including imaging, genomic sequences, and electronic health records to detect biomarkers of cancer in early stages. The statistics show that the AI-driven systems are capable to provide better diagnostic outcomes than conventional methods in some fields including mammography for breast cancer and CT for lung. Moreover, AI’s integration in genomic studies helps in determining Cancer related genes and biomarkers hence supporting precision medicine that adapts treatment to the specific genetic information of the patient. Apart from having outlets in AI, big data analytics, cloud computing, and IoT are equally important in early cancer detection as well. Big data analysis enables the analysis of large and complicated data sets with the aid of which one may identify inklings that may point towards possible early development of cancer. The use of cloud computing in health care mainly provides meaningful platforms for the storage and management of the large volumes of medical data in a way that allows improved efficiency and high levels of security. Wearable sensors collect data on different biomarkers throughout a patient’s body, and convey real-time information regarding whether the biomarkers’ levels are approaching cancerous state. Despite this great promise, there are various issues that have to be solved: data protection, privacy, and security, problems with the algorithms’ biases, and integration into practice. Ethical questions are generally important to tackle the uncertainty surrounding data and decision-making in clinical care using A I systems. The future trends in early cancer diagnostics will involve deeper integration of the approaches as AI and big data technology, which will enable more precise prevention and treatment. The applicability of this approach can also extend to the early identification of cancer, but also the prevention of its occurrence through proper intervention. In conclusion, conversing AI and data technologies will be useful for enhancing the efficiency of the early cancer diagnosis, and that is why the perspectives for the patients’ recovery and the further decrease in the mortality rates connected with cancer are rather promising. The approaches in this area remain informative developing technologies that are likely to be integrated into clinical work as the leading organizational models for the future of oncology and preventive health.

Development of a patient health monitoring system based on a service-oriented architecture using artificial intelligence

The object of the study is a patient health monitoring system that uses service-oriented architecture (SOA) and artificial intelligence (AI) to integrate and analyze medical data. Such a system integrates data from a variety of sources, including medical devices, health apps, electronic health records, and wearables and physiological performance recorders, providing a comprehensive approach to health monitoring. Thanks to SOA, the system is able to process large arrays of data in real time, providing the opportunity to quickly process and analyze them. This allows medical professionals to get a comprehensive picture of patients' health, taking into account both long-term trends and real-time indicators. One of the most challenging areas is ensuring effective integration and processing of disparate data from various medical devices and applications for accurate diagnosis and prognosis of diseases. It is also important to create a system that is easily scalable and can be adapted to the needs of different medical facilities and various monitoring systems. As a result of the research, it is concluded that the use of SOA allows creating flexible and scalable systems capable of integrating a wide range of medical devices and applications. The use of AI in these systems makes it possible to automatically detect deviations in health indicators, recognize pathologies in the early stages and predict the possible development of diseases. This is due to the fact that the proposed architecture has a number of features, in particular, the ability to collect, process and analyze large volumes of medical data in real time. Artificial intelligence algorithms provide high accuracy of diagnosis and forecasting thanks to the ability to quickly process complex data and find hidden patterns. Thanks to this, it is possible to obtain accurate and reliable indicators of the state of health of patients. Compared to similar known systems, it provides such advantages as increased efficiency of medical care, reduced risk of complications, early detection of diseases and a personalized approach to patient treatment, as well as the concentration of all data in one system.

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

The modern development of healthcare is characterized by a set of large volumes of tabular data for monitoring and diagnosing the patient's condition. In addition, modern methods of data engineering allow the synthesizing of a large number of features from an image or signals, which are presented in tabular form. The possibility of high-precision and high-speed processing of such large volumes of medical data requires the use of artificial intelligence tools. A linear machine learning model cannot accurately analyze such data, and traditional bagging, boosting, or stacking ensembles typically require significant computing power and time to implement. In this paper, the authors proposed a method for the analysis of large sets of medical data, based on a designed linear ensemble method with a non-iterative learning algorithm. The basic node of the new ensemble is an extended-input SGTM neural-like structure, which provides high-speed data processing at each level of the ensemble. Increasing prediction accuracy is ensured by dividing the large dataset into parts, the analysis of which is carried out in each node of the ensemble structure and taking into account the output signal from the previous level of the ensemble as an additional attribute on the next one. Such a design of a new ensemble structure provides both a significant increase in the prediction accuracy for large sets of medical data analysis and a significant reduction in the duration of the training procedure. Experimental studies on a large medical dataset, as well as a comparison with existing machine learning methods, confirmed the high efficiency of using the developed ensemble structure when solving the prediction task.

Modern methods of diagnosis of gynecological diseases

The article discusses modern methods of diagnosis of gynecological diseases. In recent years, there has been significant progress in the application of artificial intelligence (AI) in medicine, including gynecology. This paper presents an overview of modern methods of diagnosing gynecological diseases using AI. The authors consider various approaches, such as machine learning and deep learning, and describe their advantages in the context of improving diagnostic accuracy and speed. Special attention is paid to the analysis of large volumes of medical data, which allow us to create more effective diagnostic algorithms. Additionally, the potential of integrating AI into clinical practice and its impact on improving the quality of medical care for women is being considered, opening up new prospects in the field of gynecology.

The impact of artificial intelligence on medical diagnostics: A letter to the editor

Madam, Artificial intelligence (AI) describes the generation of intelligent machines that are capable of carrying out activities that usually call for human intellect. (1) It involves creating algorithms and models that enable computers to learn from and analyze vast amounts of data, recognize patterns, make decisions, and even engage in natural language processing. (2) AI has become increasingly important in various fields due to its potential to automate complex processes, enhance efficiency, and drive innovation. It can transform industries, improve decision-making, and revolutionize how we live and work. In medical diagnostics, AI has had a profound impact on improving accuracy, efficiency, and patient outcomes. By leveraging machine learning algorithms, AI systems can process and analyze large volumes of medical data, including patient records, laboratory results, medical images, and genomic data. This allows for more accurate and timely diagnoses, as AI algorithms can detect subtle patterns and anomalies that may not be visible to human observers. AI-powered diagnostics can assist healthcare professionals in detecting diseases, predicting patient outcomes, and designing personalized treatment plans. (3) Furthermore, AI can contribute to the early detection of diseases, thereby enabling timely interventions and improving patient survival rates. For example, AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to detect abnormalities and potential signs of diseases like cancer. This helps radiologists and clinicians identify and diagnose conditions early when treatment options are more effective. (4) Additionally, AI aids in interpreting genetic data, allowing for the identification of genetic markers associated with specific diseases or drug responses. This information guides clinicians in selecting the most suitable treatment options for individual patients, leading to improved therapeutic outcomes. (5) AI in medical diagnostics also faces hurdles with data privacy and accessibility, as patient information must be handled securely. Ensuring AI algorithms are unbiased, and fair is a challenge to avoid disparities in healthcare outcomes. The interpretability of AI models is vital to building trust with healthcare professionals and facilitating their adoption. Regulatory compliance and ethical considerations are also significant factors that need to be addressed for the responsible and effective integration of AI in the medical field. In conclusion, Artificial Intelligence (AI) is a transformative technology with the potential to revolutionize various industries, including medical diagnostics. Its ability to analyze vast amounts of data, recognize patterns, and make intelligent decisions has proven invaluable in improving diagnostic processes accuracy, efficiency, and effectiveness. ---Continue

Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare

The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.

Machine Learning Model for Predicting Mortality Risk in Patients With Complex Chronic Conditions: Retrospective Analysis.

The health care system is undergoing a shift toward a more patient-centered approach for individuals with chronic and complex conditions, which presents a series of challenges, such as predicting hospital needs and optimizing resources. At the same time, the exponential increase in health data availability has made it possible to apply advanced statistics and artificial intelligence techniques to develop decision-support systems and improve resource planning, diagnosis, and patient screening. These methods are key to automating the analysis of large volumes of medical data and reducing professional workloads. This article aims to present a machine learning model and a case study in a cohort of patients with highly complex conditions. The object was to predict mortality within the following 4 years and early mortality over 6 months following diagnosis. The method used easily accessible variables and health care resource utilization information. A classification algorithm was selected among 6 models implemented and evaluated using a stratified cross-validation strategy with k=10 and a 70/30 train-test split. The evaluation metrics used included accuracy, recall, precision, F1-score, and area under the receiver operating characteristic (AUROC) curve. The model predicted patient death with an 87% accuracy, recall of 87%, precision of 82%, F1-score of 84%, and area under the curve (AUC) of 0.88 using the best model, the Extreme Gradient Boosting (XGBoost) classifier. The results were worse when predicting premature deaths (following 6 months) with an 83% accuracy (recall=55%, precision=64% F1-score=57%, and AUC=0.88) using the Gradient Boosting (GRBoost) classifier. This study showcases encouraging outcomes in forecasting mortality among patients with intricate and persistent health conditions. The employed variables are conveniently accessible, and the incorporation of health care resource utilization information of the patient, which has not been employed by current state-of-the-art approaches, displays promising predictive power. The proposed prediction model is designed to efficiently identify cases that need customized care and proactively anticipate the demand for critical resources by health care providers.

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

Generalized information with examples on the possibility of using a service-oriented approach and artificial intelligence technologies in the industry of e-Health

The object of the research is the review of ways of implementing service-oriented approaches (SOA) and artificial intelligence (AI) technologies in modern healthcare systems. The generalization of these ways will allow to cope with complex modern challenges, such as increasing demand for medical services, growing volumes of data, and the need for high-quality and effective treatment. This work is aimed at this. The field of e-Health is rapidly gaining popularity and combines many different systems. But due to the large number of tools and system providers with different architectures, there is a problem that different systems are difficult or impossible to integrate and connect with each other. It is shown that the use of SOA makes it possible to break down complex systems into separate services that can interact with each other to ensure fast and accurate data processing, effective management of medical resources, and improvement of the quality of medical services. AI can be used to analyze large volumes of medical data, predict risks, diagnose diseases, and develop individualized treatment plans. The use of AI in healthcare systems helps improve diagnostic accuracy, reduce treatment times, and improve patient outcomes. The synergy of SOA and AI in health care systems is important when SOA provides the means to integrate various AI solutions, which allows for the interaction of different services and the exchange of data to ensure effective treatment and collaboration between medical professionals and artificial intelligence systems. Such distribution of systems makes it possible to scale them without affecting other services that are already running. Therefore, it becomes possible to use unified data transfer protocols and combine different services into one system without radically changing the codebase and building additional layers of abstraction for interaction between services that cannot be combined in one system. Examples of the use of SOA and AI in modern health care systems to improve the quality of medical services, optimize resources and ensure an individual and effective approach to patient treatment, which can be used at the next stages of medical reform in Ukraine, are considered.

Live laparoscopic video retrieval with compressed uncertainty.

Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach to retrieval, involving text in the form of keywords, is severely limited when dealing with complex media formats. Content-based retrieval offers a way to overcome this limitation, by using rich media as the query itself. Surgical video-to-video retrieval in particular is a new and largely unexplored research problem with high clinical value, especially in the real-time case: using real-time video hashing, search can be achieved directly inside of the operating room. Indeed, the process of hashing converts large data entries into compact binary arrays or hashes, enabling large-scale search operations at a very fast rate. However, due to fluctuations over the course of a video, not all bits in a given hash are equally reliable. In this work, we propose a method capable of mitigating this uncertainty while maintaining a light computational footprint. We present superior retrieval results (3%-4% top 10 mean average precision) on a multi-task evaluation protocol for surgery, using cholecystectomy phases, bypass phases, and coming from an entirely new dataset introduced here, surgical events across six different surgery types. Success on this multi-task benchmark shows the generalizability of our approach for surgical video retrieval.

LAGAN: Lesion-Aware Generative Adversarial Networks for Edema Area Segmentation in SD-OCT Images.

Large volume of labeled data is a cornerstone for deep learning (DL) based segmentation methods. Medical images require domain experts to annotate, and full segmentation annotations of large volumes of medical data are difficult, if not impossible, to acquire in practice. Compared with full annotations, image-level labels are multiple orders of magnitude faster and easier to obtain. Image-level labels contain rich information that correlates with the underlying segmentation tasks and should be utilized in modeling segmentation problems. In this article, we aim to build a robust DL-based lesion segmentation model using only image-level labels (normal v.s. abnormal). Our method consists of three main steps: (1) training an image classifier with image-level labels; (2) utilizing a model visualization tool to generate an object heat map for each training sample according to the trained classifier; (3) based on the generated heat maps (as pseudo-annotations) and an adversarial learning framework, we construct and train an image generator for Edema Area Segmentation (EAS). We name the proposed method Lesion-Aware Generative Adversarial Networks (LAGAN) as it combines the merits of supervised learning (being lesion-aware) and adversarial training (for image generation). Additional technical treatments, such as the design of a multi-scale patch-based discriminator, further enhance the effectiveness of our proposed method. We validate the superior performance of LAGAN via comprehensive experiments on two publicly available datasets (i.e., AI Challenger and RETOUCH).

BMIF: Privacy-preserving Blockchain-based Medical Image Fusion

Medical image fusion generates a fused image containing multiple features extracted from different source images, and it is of great help in clinical analysis and diagnosis. However, training a deep learning model for image fusion usually requires enormous computing power, especially for large volumes of medical data. Meanwhile, the privacy of images is also a critical issue. In this article, we propose a privacy-preserving blockchain-based medical image fusion (BMIF) framework. First, to ensure fusion performance, we design a new medical image fusion model based on convolutional neural network and Inception network and integrate the proposed model into the consensus process of blockchain. Next, to save computing power of blockchain, we design a consensus mechanism by requesting consensus nodes to train the fusion model instead of calculating useless hash values in traditional blockchain. Then, to protect data privacy, we further present an efficient homomorphic encryption to realize the training of fusion model on encrypted medical data. Finally, we conduct theoretical analysis and extensive experiments on public datasets to evaluate the feasibility and the performance of our proposed BMIF. The results exhibit that BMIF is efficient and secure, and our medical image fusion network performs better than state-of-the-art approaches.

The role of artificial intelligence in cardio-oncology: present and future: A review

Cardiooncology has been developed as a relatively new branch of medicine that focuses on the prevention and treatment of adverse cardiovascular events associated with cancer therapy. Newer, more efficient data acquisition methods, such as the use of artificial intelligence, are desirable to help assess CVR in cancer patients. By extracting hidden patterns and evidence from large volumes of medical data, artificial intelligence can create new predictors and parameters to predict risks in patients with cardio-oncological diseases.

Quality-Driven Energy-Efficient Big Data Aggregation in WBANs

In the Internet of Things (IoT) era, the development of wireless body area networks (WBANs) and their applications in big data infrastructure has gotten a lot of attention from the medical research community. Since sensor nodes are low-powered devices that require heterogeneous quality of service, managing large amounts of medical data is critical in WBANs. Therefore, effectively aggregating a large volume of medical data is important. In this context, we propose a quality-driven and energy-efficient big data aggregation approach for cloud-assisted WBANs. For both the intra-BAN (Phase I) and inter-BAN (Phase II) communications, the aggregation approach is cost effective. Extensive simulation results show that quality-driven energy-efficient big data aggregation for WBANs improves network efficiency in terms of traffic served and energy consumption by 5–7 and 7–8% as compared to the existing schemes.

SDH: Secure Data Hiding in Fused Medical Image for Smart Healthcare

Fusing the single modality medical images to obtain a distinct multimodality image is needed to ensure a better clinical experience. However, the distribution of these fused images brings the issues of ownership and authentication and has attracted many researchers. Presently, large volumes of medical data are stored on cloud platforms. However, outsourcing medical data to this popular platform may introduce security issues. Following this, we introduce a secure data hiding in fused medical image for smart healthcare, since it is also suitable for applications in the cloud. To achieve this, we first create a fused medical image as a cover by nonsubsampled contourlet transform (NSCT). The method, which is based on NSCT, QR, and Schur decomposition, allows concealing the image and electronic patient records (EPR) mark into the fused image. Importantly, EPR watermark includes a hash value of cover is created first and then embedded into the cover via magic cube-based procedure. Finally, the marked image is encrypted using deoxyribonucleic acid (DNA), chaotic maps, and a hash function-based encryption scheme. The introduced watermarking scheme has been evaluated using 25 pairs of medical images and several embedding/extracting parameters. Apart from being satisfactorily imperceptible, the proposed work is also robust and secure against well-known signal processing attacks and promising results are obtained when compared with similar techniques. It indicates a considerable improvement in robustness of 66.7% and 99.7% over existing discrete wavelet transform (DWT)–singular value decomposition (SVD)-based watermarking schemes.

Machine learning for contour classification in TG-263 noncompliant databases.

A large volume of medical data are labeled using nonstandardized nomenclature. Although efforts have been made by the American Association of Physicists in Medicine (AAPM) to standardize nomenclature through Task Group 263 (TG‐263), there remain noncompliant databases. This work aims to create an algorithm that can analyze anatomical contours in patients with head and neck cancer and classify them into TG‐263 compliant nomenclature. To create an accurate algorithm capable of such classification, a combined approaching using both binary images of individual slices of anatomical contours themselves, as well as center of mass coordinates of the structures are input into a neural network. The center of mass coordinates were scaled using two normalization schemes, a simple linear normalization scheme agnostic of the patient anatomy, and an anatomical normalization scheme dependent on patient anatomy. The results of all of the individual slice classifications are then aggregated into a single classification by means of a voting algorithm. The total classification accuracy of the final algorithms was 97.6% mean accuracy per class for nonanatomically normalization scheme, and 97.9% mean accuracy per class for anatomically normalization scheme. The total accuracy was 99.0% (13 errors in 1302 structures) for the nonanatomically normalization scheme, and 98.3% (22 errors in 1302 structures) for the anatomically normalization scheme.

Lossless medical image compression based on anatomical information and deep neural networks

Modern imaging modalities generate large volumes of medical data that place a heavy burden on both storage and transmission. Consequently, image data compression is a key research topic in the field of medical imaging. This paper proposes a compression technique that combines anatomical information and a custom deep neural network model to facilitate the transmission and storage of medical image data. This technique first divides the medical image data into specific regions based on anatomical features, namely, image density, relative position, and organ size. A deep neural network is then trained to generate a series of optimal predictors for each region. These predictors can be adaptively switched based on the characteristics of the region being compressed. The residuals are finally compressed with an entropy coding scheme. An evaluation of this compression technique shows that the proposed “divide and conquer” method indeed achieves high prediction accuracy and obtains a compression performance that is better than that of JPEG2000 by 38%.

Deep enriched salp swarm optimization based bidirectional -long short term memory model for healthcare monitoring system in big data

The rapid development of communication technologies and expert systems have resulted in a large volume of medical data. Big data such as clinical data, omics data, and electronic health data are difficult to manage in real-time due to noise, large size, different formats, missing values and large features. Hence, it is more difficult for the health monitoring system to extract the correct information. Low quality and noisy data can lead to unnecessary treatment. To overcome these issues, we proposed Enriched Salp Swarm Optimization based Bidirectional Long Short Term Memory (ESSOBiLSTM) to monitor health. This method consists of four layers, such as the data collection layer, data storage layer, data analytics, and presentation layer. The initial layer handles a variety of information from main sources: wearable sensor devices (WSD), social network data, and medical records (MR). The second layer stores all the collected data from WSD, MR, and social network data to the cloud server through the wireless network. The proposed framework for performing big data analytics steps like preprocessing, filtering, dimensionality reduction, and classification is performed in the third layer. In the final layer, the doctor analyzes the patient's condition based on the classification results of the enriched SSO-BiLSTM. Based on the evaluation report, the proposed ESSOBiLSTM gives an accuracy of 85%, precision of 80%, RMSE of 0.6, MAE of 0.58, recall of 85% and F-measure of 79%. As a result, ESSOBiLSTM has proven to be more effective in monitoring health in large datasets.

Cloud based secure watermarking using IWT-Schur-RSVD with fuzzy inference system for smart healthcare applications

The healthcare records include personal and sensitive information about the patient. These records are easily distributed via different platform for clinical diagnosis purpose. Presently, large volumes of medical data are stored on cloud platform. However, outsourcing medical data to these popular platforms may introduce security issues. Thus, a secure watermarking algorithm based on integer wavelet transform (IWT)-Schur-Randomized Singular Value Decomposition (RSVD) is put forward for medical records sharing in the cloud environment. To ensure high level of authentication, discrete wavelet transform (DWT) based embedding is conducted to achieve dual watermarking by concealing system MAC address in the logo image. Prior to this, the system address is encoded via turbo code to reduce /eliminate the channel noise, if any. Further, both invisibility and robustness are achieved by fuzzy inference system (FIS), and the marked image is encrypted using chaotic encryption scheme to further improve the security. Furthermore, experimental results is reported and compared with the existing works which demonstrated promising outcomes, indicating its potential towards realization of a value-added tool for smart healthcare applications.