Privacy Of Health Information Research Articles

Students health physique information sharing in publicly collaborative services over edge-cloud networks

Data privacy is playing a vital role while facing the digital life aspects. Today, the world is being extensively inter-connected through the internet of things (IoT) technologies. This huge interconnectivity is bringing very wonderful capabilities for improving the quality of life (QoL) with itself, for instance, in distributed healthcare. On the other hand, there are new challenges in the interconnectivity per use. One of the most challenging issues of IoT use in social systems and digital life is secure, trustable, and reliable interactions over IoT networks such that safety, security, and privacy in both aspects of cyber and physical worlds for humankind should be planned and controlled.Due to the less physical activity of most people in the current world, fitness and aerobic sports are now an important need at any age to help them keep healthy in their cyber-physical life, specifically, for the younger student that are still in the growth ages. However, these sport activities need to be monitored seriously and closely to not put their life in danger. Herewith, healthcare services through IoT is becoming more applicable. Therefore, health information privacy for athletes is now a hot topic of investigation as we present the topic here. We propose an IoT-based physique healthcare system considering private information sharing for athletes based on data hiding at the edge of a collaborative system. The proposed system pays attention to the key factors of healthcare IoT infrastructure but it is bringing its new suggestions for more safety. Moreover, many evaluations based on different kinds of healthcare data are provided.

Scoping Review of Barriers and Facilitators to Recruitment of Black People With Cancer in Biospecimen-Based Research.

The increasing focus on precision medicine to optimize cancer treatments and improve cancer outcomes is an opportunity to consider equitable engagement of people racialized as Black or African American (B/AA) in biospecimen-based cancer research. B/AA people have the highest cancer incidence and mortality rates compared with all other racial and ethnic groups in the United States, yet are under-represented in biospecimen-based research. A narrative scoping review was conducted to understand the current literature on barriers, facilitators, and evidence-based strategies associated with the engagement of B/AA people with cancer in biospecimen research. Three comprehensive searches of MEDLINE, CINAHL, Embase, and Scopus were conducted. Of 770 studies generated by the search, 10 met all inclusion criteria for this review. The most frequently reported barriers to engagement of B/AA people in biospecimen research were lack of biospecimen research awareness, fear of medical harm, and violation of personal health information privacy, resource constraints, and medical mistrust. Key facilitators included previous exposure to research, knowledge about underlying genetic causes of cancer, and altruism. Recommended strategies to increase participation of B/AA people in biospecimen-based research included community engagement, transparent communication, workforce diversity, education and training, and research participant incentives. Inclusion of B/AA people in biospecimen-based research has the potential to advance the promise of precision oncology for all patients and reduce racial disparities in cancer outcomes.

Unpacking Sociotechnical Discourses on Telehealth Use and Data Protection: A Path Towards Digital Health Value Creation.

Telehealth uptake will remain sub-optimal without consumer trust. Safeguarding the security and privacy of health information plays an important role in building trust and acceptance of telehealth. This study seeks to unpack the sociotechnical discourses on the use of telehealth with a focus on privacy and security in the context of United States health services. A search of the media outlets facilitated via the Factiva database was conducted. Using a qualitative method, thematic analysis was performed on the news texts to identify the key themes and provide contextual explanations. The analysis led to the identification of three key themes: 'data protection practice', 'clinical resilience', and 'digital health business value' perspectives. These themes focus on various concepts of telehealth use including data privacy, security, public health emergency, compliance activities in the use of telehealth, meeting stakeholders' needs, reducing costs of service delivery, the potential of telehealth for informed action, and improving users' experience. Among these themes, 'data protection practice' was directly associated with privacy compliance and telehealth use. Other thematic discourses have provided an indirect reflection on the role of privacy compliance, with a greater emphasis placed on health service delivery and market dynamics rather than compliance in practice. Our study revealed the importance of the COVID-19 pandemic in telehealth use, highlighting the move towards 'good faith' and responsible use of telehealth.

Emerging Trends in AI-Driven Health Tech: A Comprehensive Review and Future Prospects

Purpose: The purpose of this research is to explore the integration of artificial intelligence (AI) in healthcare, specifically within the realm of health informatics (HI). The study aims to understand the impact of AI on patient treatment, research, and operational processes within healthcare systems. Additionally, it seeks to address the challenges posed by the increasing volume of unorganized and unstructured data generated by AI technologies in healthcare.
 Materials and Methods: This research employs a comprehensive analysis approach, utilizing complex health information systems, clinical images, and intricate language. It examines the current utilization of AI in healthcare, focusing on its effects on patient and clinician involvement in healthcare decision-making. The assessment emphasizes key skill areas of Health Informatics, including IT, health information systems, security and privacy, telemedicine, m-Health, consumer health informatics, and clinical informatics.
 Findings: The study identifies several significant findings regarding the role of AI in healthcare. It highlights how AI technologies contribute to the generation of unstructured data, posing challenges for research and analysis. Additionally, the research underscores AI's ability to enhance personalized medical guidance, identify complex illnesses, forecast negative health occurrences, and improve patient outcomes. Moreover, it discusses AI's impact on social media and mobile apps, emphasizing its potential to gather valuable insights from online sources for a deeper understanding of patient needs and behaviors.
 Implications to Theory, Practice and Policy: Based on the findings, the research suggests several recommendations for future research and progress in the field of AI usage in healthcare. These recommendations may include further exploration of AI applications in healthcare decision-making, addressing challenges related to unstructured data, enhancing security and privacy measures, and leveraging AI for improving patient outcomes and clinician engagement. Additionally, the study emphasizes the importance of ongoing research and development to maximize the potential benefits of AI in healthcare.

Cloud-based secure data storage and access control for internet of medical things using federated learning

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

HHS Office for Civil Rights opens investigation into UnitedHealthcareGroup cyberattack

Last week, the U.S. Department of Health and Human Services' Office for Civil Rights (OCR) issued a “Dear Colleague” letter addressing the cybersecurity incident impacting Change Healthcare, a unit of UnitedHealthcare Group (UHG), and many other health care entities. The cyberattack is disrupting health care and billing information operations nationwide and poses a direct threat to critically needed patient care and essential operations of the health care industry. OCR enforces the HIPAA Privacy, Security, and Breach Notification Rules, which require that HIPAA covered entities – virtually all health care providers, clearinghouses, and insurance companies – protect the privacy and security of protected health information. The likelihood that substance use disorder (SUD) treatment information was included in the breach is very likely. However, OCR does not mention 42 CFR Part 2, just HIPAA. HHS views ransomware and hacking as the “primary cyber threats in health care.” There has been a 256% increase in large breaches involving hacking, and a 264% increase in ransomware, over the last 5 years, and this only includes those breaches OCR was notified of. $22 million was reportedly paid to the Change Healthcare hackers, in the form of bitcoin, likely from UHG, according to news reports. HHS OCR says: “If you believe that your or another person's health information privacy or civil rights have been violated, you can file a complaint with OCR at https://www.hhs.gov/ocr/complaints/index.html.” The one bright side of the new Part 2 rules are that OCR now enforces the regulation.

The birth of femtech lays fertile grounds for pregnancy discrimination in the workplace

AbstractFemtech refers to electronic devices, software, or other technology relating to women's health. Employers are contributing to the rapid growth of femtech by adopting femtech products for workplace wellness programs. Due to a lack of federal laws or regulations, employers can access the personal, intimate information their employees record in these femtech products which can subject employees to pregnancy discrimination. Congress must enact a comprehensive consumer rights law and give the Federal Trade Commission more plenary power to prevent femtech companies from selling consumers' private health information to employers.

Assessing Security Mechanisms for Protecting Patient Health Information at the Berekum Holy Family Hospital.

INTRODUCTION The security and privacy of patient health information remains a major concern for most health facilities in recent times where patient data can easily be shared between institutions. Patient health information is considered by many as one of the most confidential of all types of personal information. Protecting this confidentiality is therefore essential if the privacy of subjects of care is to be maintained. Securing patient health information involves security mechanisms versus any party not authorized to access the data. Security and privacy in electronic health records systems can be seriously threatened by hackers, viruses, and worms. According to studies carried out in several countries, concerns regarding data security and privacy have appeared. A recent study estimated that each year there are 25 million compelled authorizations for the disclosure of health records in the United States. In order to prevent these concerns, organizations such as the Certification Commission for Healthcare Information Technology (CCHIT) offer a certified program which covers a rigorous inspection of, among other things, security aspects based on existing standards, which is relevant for the United States. There is a real concern about both people’s and entities’ access levels to patient health information. A patients’ health information might be expose and accessible from several networks or devices in the health facility. (by visiting different doctors’ offices, departments, providers, etc. Security defects in some of these areas could cause the disclosure of information to unauthorized persons or companies. Patient health information therefore need protection against manipulations, unauthorized accesses and abuses, which includes taking into account training on the security techniques, encryption of health information, user-authentication and authorization. Patient health information might be tempered with to the extent that administrative staff could for example access information without the patient’s explicit consent in a financial and billing reviews. According to the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, patients’ privacy violations and breaches are becoming more expensive than ever Therefore, there is the need to assess the various security mechanisms for protecting patient health information from unauthorized access to improve the quality of care delivery.

Automatic Diagnosis of Schizophrenia in EEG Signals using CNN Models

This study uses convolutional neural networks (CNNs) to analyze electroencephalogram (EEG) signals to tackle the crucial problem of early schizophrenia diagnosis. In the study, two different CNN models with different architectural characteristics are compared and contrasted, with a focus on measures related to accuracy, loss, selectivity, and specificity. A meticulously selected dataset is obtained and preprocessed, containing EEG signals from people with and without schizophrenia. The chosen CNN architectures differ in terms of activation functions, filter sizes, and layer depth. The performance of the models is carefully assessed using metrics appropriate to the particulars of the problem, such as accuracy, loss, selectivity (true positive rate), and specificity (true negative rate), after they have undergone rigorous training on the preprocessed dataset. The goal of the comparison analysis is to offer a thorough grasp of how various architectural decisions affect the model’s capacity to correctly identify patterns linked to schizophrenia in EEG data. In addition to examining optimization and fine-tuning techniques to improve the model’s performance, the research also focuses on ethical issues to guarantee the responsible use of private health information. In the end, this work aims to further the development of deep learning-based EEG signal analysis diagnostic tools for schizophrenia, with a focus on the selection of assessment metrics that are important benchmarks for judging model performance. Keywords: schizophrenia, mental disorders, machine learning, biomedical signals, EEG signals, CNN

Graduate students’ privacy boundaries in communicating about mental health with their advisors

ABSTRACT The purpose of this study is to understand the decision-making process that graduate students engage in when deciding whether to withhold or disclose information about mental health to their academic advisors. Guided by the principles of the phronetic iterative approach and Communication Privacy Management Theory, a thematic analysis of open-ended survey responses from 81 participants currently enrolled in graduate education revealed several privacy boundary rules used to determine whether to disclose private health information with advisors. Graduate students in this study enacted three motivational criteria (i.e., help-seeking, transparency, and relationship-building), two contextual criteria (i.e., relational closeness and relevance), and four risk–benefit ratios (i.e., anticipated advisor response, stigma, student emotional response, and advisor mental health) in creating privacy rule boundaries. These rules are discussed in terms of both theoretical and practical implications for the graduate student mental health crisis in the United States.

Cognizance About Privacy and Security of Patients Health Information among Medical and Dental Students

Background: The proliferation of electronic health records and digital medical systems has brought about significant advancements in patient care and information management. However, concerns regarding the privacy and security of patients' health information have also intensified. As future healthcare practitioners, medical and dental students play a pivotal role in ensuring the safeguarding of sensitive patient data. This study aims to assess the awareness and understanding of privacy and security issues related to patients' health information among medical and dental students. Methods: A cross-sectional study was conducted among a representative sample of medical and dental students from diverse academic institutions. A structured questionnaire was administered to assess participants' knowledge, attitudes, and practices concerning patient data privacy and security. Descriptive statistics were used to analyse the data, and associations between demographic factors and awareness levels were explored. Results: The study findings reveal that while a substantial portion of medical and dental students recognize the importance of patient data privacy, a significant knowledge gap exists regarding the specific measures and protocols to ensure security. The research found that 232(71.7%) of the participants preferred electronic patient records for their ease of accessibility and 198 (61.4%) strongly valued privacy. Students expressed varying levels of understanding concerning encryption methods, access controls, and legal frameworks governing patient information. Conclusion: This study underscores the imperative for comprehensive education and training programs focusing on privacy and security of patients' health information within medical and dental curricula. As future healthcare practitioners, medical and dental students must be equipped with the necessary skills and knowledge contributing to a more secure and privacy-aware healthcare environment.

Investigating social determinants of child health and their implications in reducing pediatric traumatic injury: A framework and 17-year retrospective case-control study protocol.

Traumatic physical injuries are the number one cause of hospitalization and death among children in Canada. The majority of these injuries are preventable. The burden from injury can be reduced through prevention programs tailored to at-risk groups, however, existing research does not provide a strong explanation of how social factors influence a child's risk of injury. We propose a theoretical framework to better understand social factors and injury in children and will examine the association between these social factors and physical traumatic injury in children using large population-wide data. We will examine data from 11,000 children hospitalized for traumatic physical injury and 55,000 matched uninjured children by linking longitudinal administrative and clinical data contained at the Manitoba Centre for Health Policy. We will examine 14 social determinants of child health measures from our theoretical framework, including receipt of income assistance, rural/urban status, socioeconomic status, children in care, child mental disorder, and parental factors (involvement with criminal justice system, education, social housing, immigration status, high residential mobility, mother's age at first birth, maternal Axis I mental disorder, maternal Axis II mental disorder and maternal physical disorder) to identify groups and periods of time when children are at greatest risk for traumatic physical injury. A conditional multivariable logistic regression model will be calculated (including all social determinant measures) to determine odds ratios and adjusted odds ratios (95% confidence interval) for cases (injured) and controls (non-injured). Health Information Privacy Committee (HIPC No. 2017/2018-75) and local ethics approval (H2018-123) were obtained. Once social measures have been identified through statistical modelling, we will determine how they fit into a Haddon matrix to identify appropriate areas for intervention. Knowing these risk factors will guide decision-makers and health policy.

Telehealth and Teleradiology: What You Need to Know

Telehealth is the use of communication technologies via one or more modalities to provide remote healthcare services. Standards for etiquette, federal and state policies, health information privacy, licensure, and billing and reimbursement direct all telehealth services. The expansion of teleradiology has increased access to diagnostic imaging and interpretation in rural and underserved areas.

ZeroTrustBlock: Enhancing Security, Privacy, and Interoperability of Sensitive Data through ZeroTrust Permissioned Blockchain

With the digitization of healthcare, an immense amount of sensitive medical data are generated and shared between various healthcare stakeholders—however, traditional health data management mechanisms present interoperability, security, and privacy challenges. The centralized nature of current health information systems leads to single points of failure, making the data vulnerable to cyberattacks. Patients also have little control over their medical records, raising privacy concerns. Blockchain technology presents a promising solution to these challenges through its decentralized, transparent, and immutable properties. This research proposes ZeroTrustBlock, a comprehensive blockchain framework for secure and private health information exchange. The decentralized ledger enhances integrity, while permissioned access and smart contracts enable patient-centric control over medical data sharing. A hybrid on-chain and off-chain storage model balances transparency with confidentiality. Integration gateways bridge ZeroTrustBlock protocols with existing systems like EHRs. Implemented on Hyperledger Fabric, ZeroTrustBlock demonstrates substantial security improvements over mainstream databases via cryptographic mechanisms, formal privacy-preserving protocols, and access policies enacting patient consent. Results validate the architecture’s effectiveness in achieving 14,200 TPS average throughput, 480 ms average latency for 100,000 concurrent transactions, and linear scalability up to 20 nodes. However, enhancements around performance, advanced cryptography, and real-world pilots are future work. Overall, ZeroTrustBlock provides a robust application of blockchain capabilities to transform security, privacy, interoperability, and patient agency in health data management.

Efficient Key Generation Techniques for Wireless Body Area Network

In wireless body area network health care applications, energy-constraint wearable devices are used to monitor patient physiological parameters. The security of the private health information of a person plays a significant role because if it is captured and read by an unauthorized person, the confidentiality of the patient data is lost. Therefore, there is a requirement to secure the data by performing encryption to transfer it into an unreadable form. Since the resources used for encryption should be kept to a minimum as the devices are attached to the human body, a lightweight encryption algorithm has to be used. Therefore, the generation of a unique key used for encryption plays a significant role. In work, generating a unique key uses the ECG values taken from MIT-BIH Arrhythmia database. Four unique keys are generated, which can be used for encryption. The uniqueness and randomness of the keys generated are proved using the runs test and frequency test within the block. Also, the average hamming distance calculated between the ECG keys generated from two different ECG signals is 62.5% (approx 80 bits), which proves the distinctiveness of the keys generated. Implementation of the work is performed using Matlab.

Intrusion Detection in Wireless Body Area Network using Attentive with Graphical Bidirectional Long-Short Term Memory

Recently developed low-power networked systems, wireless communications, and wireless sensors have all contributed to the rise of Wireless Sensor Networks (WSNs) as a potentially useful tool in the medical field. Securing Wireless Body Area Networks (WBANs) is essential for their widespread use in healthcare environments because the data they send frequently includes private and confidential patient health information. The study's goal is to create a system for detecting intrusions in WBAN. To best identify attacks in such systems, we present a novel “Attention-based Bi-directional Long Short-Term Memory with Graph Construction” (ABL-GC) here. The suggested approach ensures that the intrusion detection system uses only the features essential to detect a given attack, reducing the processing complexity.

Considerations for privacy and confidentiality in adolescent health care service delivery.

A hallmark of delivering quality adolescent health care services is the provision of confidential care. Key tenets when providing confidential care for adolescents include time alone with a health care provider, maintaining the privacy of health information, and securing informed consent for services without permission from a parent, guardian, or caregiver. While confidentiality is a basic principle for all health care encounters regardless of age, the unique considerations for capable adolescent patients are not always realized or appreciated. By ensuring appropriate quantity and quality of confidential care for adolescents, clinicians are better equipped to elicit a comprehensive history and physical examination, while empowering the adolescent involved to develop agency, autonomy, trust, and responsibility for their own health care decision-making and management.

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis.

The Supreme Court ruling in Dobbs v Jackson Women's Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. This study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information. We scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest. Our study revealed (1) a transient increase in the number of daily users by 576.86% (95% CI 545.34%-607.92%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI -22.98% to -16.59%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information-related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information-related terms and concomitant negative sentiment in the postdecision leak period. The study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information-related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services.

Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review

This paper reviews nationally representative public opinion surveys on artificial intelligence (AI) in the United States, with a focus on areas related to health care. The potential health applications of AI continue to gain attention owing to their promise as well as challenges. For AI to fulfill its potential, it must not only be adopted by physicians and health providers but also by patients and other members of the public. This study reviews the existing survey research on the United States' public attitudes toward AI in health care and reveals the challenges and opportunities for more effective and inclusive engagement on the use of AI in health settings. We conducted a systematic review of public opinion surveys, reports, and peer-reviewed journal articles published on Web of Science, PubMed, and Roper iPoll between January 2010 and January 2022. We include studies that are nationally representative US public opinion surveys and include at least one or more questions about attitudes toward AI in health care contexts. Two members of the research team independently screened the included studies. The reviewers screened study titles, abstracts, and methods for Web of Science and PubMed search results. For the Roper iPoll search results, individual survey items were assessed for relevance to the AI health focus, and survey details were screened to determine a nationally representative US sample. We reported the descriptive statistics available for the relevant survey questions. In addition, we performed secondary analyses on 4 data sets to further explore the findings on attitudes across different demographic groups. This review includes 11 nationally representative surveys. The search identified 175 records, 39 of which were assessed for inclusion. Surveys include questions related to familiarity and experience with AI; applications, benefits, and risks of AI in health care settings; the use of AI in disease diagnosis, treatment, and robotic caregiving; and related issues of data privacy and surveillance. Although most Americans have heard of AI, they are less aware of its specific health applications. Americans anticipate that medicine is likely to benefit from advances in AI; however, the anticipated benefits vary depending on the type of application. Specific application goals, such as disease prediction, diagnosis, and treatment, matter for the attitudes toward AI in health care among Americans. Most Americans reported wanting control over their personal health data. The willingness to share personal health information largely depends on the institutional actor collecting the data and the intended use. Americans in general report seeing health care as an area in which AI applications could be particularly beneficial. However, they have substantial levels of concern regarding specific applications, especially those in which AI is involved in decision-making and regarding the privacy of health information.

Breast cancer detection from histopathological image dataset using hybrid convolution neural network

Cancer of the breast is a deadly disease that can take a person’s life in many different ways. Predicting breast cancer at an early stage is crucial in the fight to end the disease. The usage of deep learning and blockchain technology has been implemented with the intention of integrating optimal prediction with clinical diagnostics and protecting private health information. Patients’ medical records are encrypted and stored in the blockchain for maximum safety. As a result, a large portion of time and energy is spent on feature engineering, a tedious process that requires prior expert domain knowledge of the data to develop effective features, and is crucial to the success of most conventional classification systems. Deep learning, on the other hand, can arrange the discriminative information in the data without the need for a domain expert to develop feature extractors. The research community and industry have paid attention to deep, feedforward networks like convolutional neural networks (CNNs) because of their empirical results in areas including speech recognition, signal processing, object recognition, natural language processing, and transfer learning. For the best breast cancer prediction, a new method called a “Hybrid CNN” combining the Sine Cosine Algorithm (SCA) with a transfer learning algorithm has been presented. Mini-batch size and drop-out rate are just two of the factors that the SCA algorithm may fine-tune. To stop the model from overfitting, we employ a transfer learning strategy. The hyperparameters found using sine cosine algorithm is used in Visual geometry Group (VGG 16) architecture. ImageNet is used to pretrain the network and last three convolutional layers are trained using transfer learning. The integration of sine cosine algorithm and transfer learning together increases the accuracy thereby preventing the model from overfitting. The experimentation is performed in Google Colab and the proposed Hybrid CNN is compared with existing methodologies such as K-NN, SVM. Also, the proposed Hybrid CNN is compared with CNN without transfer learning and CNN without SCA. The metrics taken into account for comparison are accuracy, sensitivity, specificity, F-score. The proposed Hybrid CNN achieves 96.9% accuracy that shows the effectiveness of the integration of SCA and transfer learning.