Big data analysis for heart disease detection system using map reduce technique

Background Wearable devices capable of single-lead electrocardiograms (ECGs) are becoming increasingly prevalent. Exploring their potential for screening conditions beyond atrial fibrillation presents a compelling research opportunity. Objective This study evaluates the agreement between the Apple Watch Series 6 (APW) and simultaneous 12-lead ECG (12L) recordings in measuring PR, QRS, RR, QT, and QTc intervals in a cohort of National Health Service (NHS) cardiology patients. Methods We analysed data from the WEAR-TECH study, which included 400 simultaneous recordings from the APW and 12L ECGs. We included patients with ECGs in sinus rhythm and excluded patients with poor-quality ECGs. No exclusions were made based on underlying cardiac conditions. Measurements of the best three consecutive waveforms in leads I, II, and V5 were performed using EP Calipers (EP Studio) by a single observer. Leads I and II were compared for PR, QRS, and RR intervals, while leads I, II, and V5 were compared for QT and QTc intervals. QTc was calculated using Bazett’s formula. Differences between APW and 12L ECGs were assessed using linear mixed effects regression models with random intercepts and type II Wald chi-square tests. Clinical agreement was measured by averaging the waveforms from 12L leads and the APW and calculating the difference between the means. Results Out of 324 paired ECGs from 162 patients, 8 were excluded due to missing or uninterpretable data. The median age was 64 (IQR: 54 - 74) years, with 117 (75%) being male. PR, QRS, and RR intervals from the APW agreed best with 12L lead II, and QT and QTc intervals from APW agreed best with 12L lead V5, with no significant difference found in any of these comparisons. PR and RR intervals significantly differed between APW and 12L lead I, with the APW tending to overestimate relative to the 12L. Additionally, significant differences were found in 12L leads I and II for QT and QTc, with APW also tending to overestimate these intervals (see Table 1). Clinical agreement between the APW and all 12L leads was a minimum of 21% for a <10ms difference, 45% for a <20ms difference, and 71% for a <40ms difference (see Figure 1). Conclusions The Apple Watch Series 6 demonstrates promising potential for initial outpatient monitoring of PR, QRS, RR, QT, and QTc intervals with reasonable agreement to 12L ECG. This is especially true in lead II for PR, QRS, and RR intervals and lead V5 for QT and QTc intervals. Further reductions in variance between APW and 12L ECG measurements could enhance clinical decision-making accuracy.Table 1:The p-values of APW vs 12L leadFigure 1:Agreement of APW to 12L leads

This work presents a novel concept known as Heart Disease Systematic Machine learning Analytical Risk prediction Technology (HD-SMART) that is considered a unique perspective in handling cardiac disease and the identification of sophisticated risk markers using superior computational technology. The work offers a rather peculiar approach to harnessing multiple machine learning algorithms in combination with feature selection methods that proved themselves highly accurate in cardiovascular risk estimation. The novelty is that these algorithms, namely, the Random Forest, Support Vector Machines (SVM), and Logistic Regression, are to be adopted in a systematic manner with supplementary feature engineering and with hyperparameter optimization, respectively. Based on the UCI Heart Disease dataset of 303 instances with 14 attributes, the new HD-SMART framework illustrated exceptional predictive capability, with the Random Forest at 97.57%, followed by SVM (95.23%) and Logistic Regression at 94.18%. The methodology achieved unique convergent optimization regarding feature selection with a minimum cost function of 0.0004 at iteration 50, while the Root Mean Square Error convergence was reached within the first four iterations with a value of 0.030. The innovative approach of data preprocessing and feature analysis in the framework pointed out critical patterns in cardiological parameters, such as in chest pain distribution (n ≈ 410 typical angina cases), bimodal blood pressure peaks (130–140 mmHg and 190–200 mmHg), as well as electrocardiogram variabilities (450 normal and 350 ST-T-wave abnormalities). The new proportion of 70–30 train-test split ratio was more optimal for model performance. This work introduces a completely new, computational diagnostic approach that not only outperforms but significantly surpasses conventional methods, and its robust statistical validity is maintained across a number of performance metrics. HD-SMART contributes to the advancement of cardiovascular diagnosis as a new, effective tool for early detection of heart disease for health practitioners based on big data analysis. Received: 30 May 2025 | Revised: 11 November 2025 | Accepted: 8 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Heart Disease data set that supports the findings of this study is openly available at https://doi.org/10.1007/s00521-021-06124-1 and at https://doi.org/10.1109/iciccs48265.2020.9121169, reference number [22, 24]. Author Contribution Statement Gitanjali Gupta: Conceptualization, Methodology, Writing – original draft. Meena Malik: Conceptualization, Software, Validation, Visualization. Ramandeep Sandhu: Formal analysis, Investigation. Chander Prabha: Methodology, Resources, Data curation, Writing – review & editing, Supervision. Aimin Li: Investigation, Resources, Project administration. Saurav Mallik: Writing – review & editing, Visualization, Supervision.

The rapid evolution of machine learning (ML) and big data analytics has modernized medical decision-making procedure, offering promising path for improving classification precision and ultimately, patient outcomes. This research inspects methodologies for enhancing the classification accuracy of medical decision systems by leveraging ML algorithms and big data analytics procedure. In this study, a broad evaluation of existing literature on ML applications in healthcare and medical decision-making is carried out to discover current challenges and potential areas for improvement. The research explores the integration of diverse data sources, including electronic health records (EHRs), medical imaging, genomic data, and patient-generated data, to build robust predictive models. Moreover, the research emphasizes the importance of interpretability and transparency in ML models for medical decision-making, particularly in critical healthcare settings where the rationale behind predictions is crucial. Techniques for model explainability, such as feature importance analysis and model-agnostic interpretability methods, are explored to enhance trust and adoption of ML-driven decision systems by healthcare professionals. Furthermore, the study investigates advanced ML algorithms such as deep learning, ensemble methods, and feature engineering techniques to extract meaningful patterns from large and complex medical datasets. Through experimentation with real-world medical datasets, the efficacy of these algorithms in improving classification accuracy is evaluated and compared against traditional methods. The result of this research contributes to the advancement of ML-driven medical decision systems by providing insights into strategies for improving classification accuracy, thereby facilitating more exact diagnosis, prognosis, and treatment recommendations. Ultimately, the integration of ML and big data analytics holds immense potential for revolutionizing healthcare delivery and improving patient outcomes.

Quantification of the acute effect of a low dose of red wine by nonlinear measures of RR and QT interval series in healthy subjects

Heart disease is a general term for all diseases that attack the heart as a cardiovascular system, such as Angina Pectoris, Acut Myocardial Infarction and Congestive Heart Failure. Heart disease in every country is ranked first as a non-communicable disease (NCD), the number one cause of death in the world. Heart disease can be prevented and can be detected early if each person knows the risk factors that can cause heart disease. The aim of this community service is to increase cadres' knowledge and skills regarding early detection of the risk of heart disease. The PKM methods used are: Initial survey, implementation with counseling, transfer of health knowledge and healthy living campaigns, heart disease detection, evaluation carried out during elderly posyandu activities. The results obtained were 64.3% of the knowledge of high category cadres, and they were able to apply the questionnaire for early detection of heart disease. After this service is carried out, cadres are expected to be able to apply it to residents who suffer and are at risk of suffering from heart disease.

Early detection of heart disease is essential for minimizing mortality, particularly in resource-constrained health-care environments. Recent models have shown accurate performance for medical diagnosis; however, they often struggles from limited interpretability and sensitivity to missing clinical data. To address these challenges, this paper proposes A Label-Enhanced Graph Neural Network Optimized by Elk Herd Optimization for Heart Disease Detection (HDD–LEGNN–EHOA). The input data is taken from Cleveland Heart disease data collection. Initially, data are pre-processed using Generalized Correntropy Sparse Gauss–Hermite Quadrature Filter (GCSGQF) to impute missing values. Then, the processed data is given to Secretary Bird Optimization Algorithm (SBOA) to select optimal characteristics. Then, the selected attributes fed to Label-Enhanced Graph Neural Network (LEGNN) to detect heart disease as heart disease present and heart disease absent. Elk Herd Optimization Algorithm (EHOA) to fine-tune the weight parameter of LEGNN. The newly suggested HDD–LEGNN–EHOA framework is assessed based on some metrics like accuracy, sensitivity, and computational time. Finally, the performance of HDD–LEGNN–EHOA method provides attains 25.68%, 22.54%, and 31.24% higher accuracy when compared to the existing models, respectively.

The prevalence of heart disease has been consistently increasing in five recent years. In average 15 out of 1000 people have heart disease. Currently heart disease becomes the second leading cause of death in Indonesia. Early detection will guide the appropriate treatments to increase recovery opportunity. In another hand, many healthcare facilities in Indonesia are not equipped with the cardiologist. It triggers many heart disease cases are late to handle due late detection. Thus, we propose web based early heart disease detection application prototype using Case Base Reasoning framework. It is intended to support small clinic or other healthcare facilities which have no cardiologist to provide early detection of heart disease. The application is equipped with data security to handle the data privacy of the patient. Based on the black box evaluation by the expert, it is concluded that all the provided features can be run functionally.

Although Machine Learning and Deep Learning technologies have been widely used and have shown high accuracy in many applications, including in the health field, their application in early detection of heart disease still has room for improvement. Further research is needed to enhance the accuracy and efficiency of this process. This study aims to understand and improve the process of ECG signal extraction and classification based on Machine Learning and Deep Learning. Essentially, this research aims to evaluate and compare various models, focusing on the Random Forest and Convolutional Neural Networks (CNN) models. The study reviews several related researches, especially those focusing on the process of extraction and classification of ECG signals using Machine Learning and Deep Learning. After extraction and classification of data, an evaluation and comparison process is conducted to determine the best performing model. From the research conducted, it was found that Machine Learning methods generally show an accuracy rate between 97.02% - 99.66%, with the Random Forest method having an accuracy of 97.02%. Meanwhile, the CNN method shows a higher accuracy rate, which is between 98.75% - 100%. Thus, this research confirms the superiority of CNN in this classification process, and shows potential for further use in early detection of heart disease.

This heart disease is the number one killer of Chinese residents' health. Early detection of heart disease and timely treatment are of great significance to every heart disease patient. In this article, by mining the physical index data of patients with heart disease, aiming at the problem that the optimal parameters in the traditional support vector machine model are difficult to find, particle swarm optimization is used to optimize, and a classification prediction model of heart disease based on particle swarm optimization support vector machine is established. The experimental results show that compared with the traditional support vector machine model, the optimized model improves the prediction accuracy by 1.33%, and also shortens the model training time, which helps to improve the diagnosis efficiency of heart disease.

Heart disease is a fatal disease that causes significant mortality rates worldwide. The accurate and early detection of heart diseases is the most challenging task to save valuable lives. To avoid these issues, the Deep Convolutional Generative Adversarial Network (DCGAN) model is proposed that generates synthetic cardiac images. Here, two types of heart disease datasets such as the Sunnybrook Cardiac Dataset (SCD) and the Automated Cardiac Diagnosis Challenge (ACDC) dataset are selected to choose real cardiac images for implementation. The quality and consistency of the cardiac images are enhanced by preprocessed real cardiac images. In the DCGAN model, the generator is used for converting real cardiac images into synthetic images and the discriminator is responsible for differentiating real and synthetic cardiac images by binary classification decisions. To enhance the model's robustness and generalization ability, diverse augmentation techniques are implemented. The VGG16 model is applied in this paper for the image classification task and fine-tuned its parameters to optimize model convergence. For experimental validation, some of the significance metrics such as accuracy, precision, diagnostic time, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), false positive rate (FPR), false negative rate (FNR), and mean squared error (MSE) are utilized. The extensive experimental evaluations are carried out based on this metrics and attained a performance rate of the proposed method as 98.83%, 1.17%, 3.2%, 41.78, 4.52, 0.932, and 1.6s from accuracy, FPR, FNR, PSNR, MSE, SSIM, and diagnostic time, respectively. The experimental evaluation results demonstrate that the proposed heart disease diagnosis model attains superior performances than state-of-the-art methods.

Heart disease represents a significant public health problem worldwide, and early discovery is required in order to improve clinical outcomes and decrease mortality. Retinal imaging can provide a noninvasive methodology for evaluating microvascular health, with strong associations to cardiovascular disease. This work introduces a simple deeplearning method to predict heart disease from retinal fundus images. The proposed system consists of preprocessing retinal fundus images, then extracting features with convolutional neural networks (CNN), and finally predicting heart disease using binary classification. The model displays strong predictive capabilities, demonstrating that retinal vascular patterns contain important cardiovascular risk biomarkers. The results suggest that retinal images could be an effective, scalable screening tool for early detection of heart disease.

Heart disease is one of the diseases that exposes high mortality worldwide. This conventional way of predicting heart disease is usually expensive, time-consuming, and prone to human error. Early detection of heart disease is important as it helps to prevent deaths caused by this disease. Machine learning utilization as the non-invasive means for predicting heart disease is considered as a fast and affordable method to prevent the fatality of heart disease. This work aims at utilizing Convolutional neural network (CNN) to enhance the performance of an Arrhythmia prediction model. We have built an Arrythmia prediction model using neural networks comprising multiple convolutional layers and maxpooling layers. Our proposed model is trained using the MIT-BIH Arrhythmia dataset. The model performance has been evaluated and the model achieves 98.43% of performance accuracy

Heart disease is a major cause of death worldwide. About 31% of global deaths occur due to this disease. According to the WHO report, approximately 17.9 million people worldwide die each year due to this disease. Early detection of heart disease can reduce the chance of the disease progressing to a more severe stage by providing appropriate treatment. Coronary angiography is the most precise method of detecting the disease; nevertheless, it is a painful and expensive procedure, and patients do not prefer it in the early stages. As a result, a non-invasive and reliable method that can be employed as a screening tool for disease diagnosis is required. Machine learning has influenced every element of human life in the modern era, and it has a huge impact in the health care sector also. Machine learning (ML) based decision support systems can be used to diagnose heart disease by evaluating the clinical parameters of a patient. Various ML techniques have been investigated in this paper, and a system has been designed for the diagnosis of heart disease, with an emphasis on coronary artery disease. Relevant features were selected using the Extra Tree feature selection method and classification was done by XGBoost (extreme gradient boosting), KNN (K-nearest neighbor), SVM (support vector machine), logistic regression (LR), decision tree (DT), random forest (RF) and neural network (NN) classifiers. A regularized NN model for heart disease diagnosis is also developed that has provided 84.0% accuracy and 85.0% accuracy for RF on the Kaggle dataset.

Prognostic significance of circadian variability of RR and QT intervals and QT dynamicity in patients with chronic heart failure

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis