Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review

Focus issue: Artificial intelligence in medical physics.

Background: Artificial Intelligence (AI) is increasingly revolutionizing public health surveillance, particularly in regions with constrained healthcare infrastructure. This scoping review examines the application of AI in public health surveillance across Africa, identifying existing implementations, challenges, and opportunities. AI technologies such as machine learning, natural language processing, and predictive analytics enhance epidemic intelligence by analyzing vast datasets from diverse sources, including electronic health records, social media, and environmental sensors. These AI-driven tools provide early warnings for outbreaks, improve disease surveillance, and facilitate timely public health responses. Methods: A systematic search of databases, including Pubmed, Google Scholar, Researchgate, Web of Science, Scopus, Scientific Research, African Journal of Health Informatics, International Journal of Infectious Diseases, ScienceDirect, African Journal of Biotechnology, PloS One, The Lancet, JMIMR, BMJ, and BMC. The search covers publications from January 2010 to February 2025, spanning for 15 years. A total of 1411 articles. An additional 44 records were identified through other sources after removing 201 duplicates; 1254 unique articles were screened based on titles and abstracts. One thousand one hundred and twenty-seven (1127) records were excluded as they did not meet the inclusion criteria. Then, 127 full-text articles were assessed for eligibility, and 54 full-text articles were excluded for various reasons: study in non-African location (52) Not focused on AI applications (12), challenges and opportunities, Insufficient Data (9) Finally, 54 studies were included in the qualitative synthesis. Results: Following a rigorous selection process, 54 studies were included in the qualitative synthesis. Most studies (83.33%) were published as peer-reviewed journal articles, while technical reports and theses were less common, with five (9.26%) and four (7.41%) studies, respectively. The primary focus of these studies varied: 39 (72.22%) explored AI applications in disease detection and prediction, 25 (46.30%) examined AI applications in disease surveillance, 18 (33.33%) highlighted challenges in AI adoption for healthcare, and 15 (27.78%) focused on real-time surveillance and reporting in Africa. Findings reveal that AI is actively utilized in African public health systems for disease prediction, outbreak surveillance, and resource allocation. However, several challenges hinder its full potential, including inadequate infrastructure, data privacy concerns, limited access to high-quality datasets, and a shortage of AI-trained healthcare professionals. Despite these barriers, AI presents great opportunities for strengthening health security in Africa by improving diagnostic accuracy, optimizing healthcare interventions, and enhancing real-time epidemiological analysis. Conclusion: Artificial intelligence presents a transformative opportunity for health surveillance in Africa, particularly in diagnostics and disease prediction. AI-powered tools, such as mobile diagnostic applications and predictive models, enhance healthcare accessibility in resource-limited settings by analyzing vast datasets for early disease detection. Successful implementations, including AI-driven malaria mapping and tuberculosis detection through chest X-ray analysis, HIV, cholera, Ebola, measles, Zika virus, and malaria, enabling targeted screening interventions, personalized treatment plans, and efficient resource allocation, demonstrate AI’s potential to improve public health outcomes. Despite challenges such as infrastructure limitations and data privacy concerns, AI continues to revolutionize disease monitoring and response. By leveraging machine learning for targeted interventions and efficient resource allocation, AI holds promise for a future of more proactive and effective healthcare across the continent of Africa.

Digitalization, clinical microbiology and infectious diseases

Machine learning is the process in which the computer system can automatically learn from the data. It's an application of Artificial Intelligence without being explicitly programmed. Making use of Machine learning and AI an efficient algorithm for prediction of Cardio Vascular Disease (CVD) can be done. By combing Deep learning method to the ML model, a high-level abstract feature with improved performance can be obtained. Heart disease or Cardio Vascular disease is the predominant disease all over the world. Accurate prediction of heart disease is a challenging task. The accuracy of prediction can be improved by applying Machine learning algorithms. Results with high accuracy can be produced by combining a Machine learning model with Statistical concepts. The main objective of this research is to predict high accurate CVD by applying Deep learning model combined with Machine learning algorithm. By recognizing the symptoms of the disease, people can get prompt treatment on time.

Assessing perspectives on artificial intelligence applications to gastroenterology

Emergence of New Disease: How Can Artificial Intelligence Help?

Artificial intelligence and spine: rise of the machines

This paper focuses on the application of machine learning algorithms to predict cardiovascular diseases (CVDs). Every year a large number of deaths are registered all over the world. According to the World Health Organisation, CVDs are the leading cause of high mortality in the world. One of the necessary preventive measures to reduce mortality from CVDs is the timely prediction of diseases in people at high risk of such diseases. Specially developed scales and machine learning algorithms are now being used for the timely prediction of CVDs. Background. To predict heart disease, algorithms are often used: naive Bayesian classifier (Gaussian Naïve Bayes Classificator, GNBC), k-nearest neighbours (K-Nearest Neghboors, KNN), decision tree (Decision Tree, DT). In domestic literature, there are known works devoted to the application of SWD prediction using Adam gradient algorithm in deep neural network training. One of the necessary conditions for increasing the predictive ability of a machine learning model (MLM) is the optimal selection of hyperparameters. The choice of the optimal hyperparameters is often made on the basis of empirical experience. Purpose. To explore the specific application of machine algorithms to the prediction of heart disease. Materials and methods. Scientific novelty of the work. In this research we analyse machine learning algorithms for predicting the risk of CVDs using the approach of automatic search for hyperparameters MMO. The following algorithms were used to construct MMOs: NBS, KNN, DT, Logistic Regression, Support Vector Machine (SVM), Random Foorest (RF), Complement Naïve Bayes Classificator (CNBC), Linear Discriminant Analysis (LDA), Radial Basic Function (RBF), Gradient Boost (XGBoost). To evaluate the accuracy of machine learning models we used the following indicators: mean absolute error (MAE), precision, completeness (recall), F-measure (F-beta), False Positive Rate (FPR), False Negative Rate (FNR). Additionally, visual analysis of ROC curve (receiver operating characteristic) and areas under the curve (areas under the curve, AUC) were used to analyse the results of MMO. Using AUC value allows to estimate prognostic ability of MLM. Results. The training results showed that RF and XGBoost algorithms are characterized by higher accuracy. With optimal selection of MMO parameters, the overall classification accuracy was 0.88 and 0.94 respectively. Conclusion. The application of machine learning algorithms allows predictive models to be built with high accuracy. This requires the construction of a machine learning model. The ensemble machine learning algorithms RF and XGBoost have higher accuracy rates than the following algorithms: decision trees, Bayesian classification methods, logistic regression, linear discriminant analysis.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

There is a growing drive in the chemistry community to exploit rapidly growing robotic technologies along with artificial intelligence-based approaches. Applying this to chemistry requires a holistic approach to chemical synthesis design and execution. Here, we outline a universal approach to this problem beginning with an abstract representation of the practice of chemical synthesis that then informs the programming and automation required for its practical realization. Using this foundation to construct closed-loop robotic chemical search engines, we can generate new discoveries that may be verified, optimized, and repeated entirely automatically. These robots can perform chemical reactions and analyses much faster than can be done manually. As such, this leads to a road map whereby molecules can be discovered, optimized, and made on demand from a digital code.

Background/Objectives: Obesity is a complex disorder that causes further health issues linked to several chronic diseases, such as cancer, diabetes, metabolic syndrome, and cardiovascular diseases; thus, it is critical to identify and diagnose obesity as soon as possible. Traditional methods, such as anthropometric measures, were popular, although recent advances in artificial intelligence (AI) offer new opportunities for prediction models; as a result, AI has become an essential tool in obesity research. This study provides a comprehensive analysis of the research on the impact of AI on obesity prevention. Methods: In this study, the researchers performed a scoping study using AI to assess and predict obesity in PubMed, Scopus, Web of Science, and Google Scholar from February 2009 to July 2025. The researchers compiled and arranged the employed AI approaches to find connections, patterns, and trends that could guide further research and the application of machine learning algorithms for advanced data analytics. Results: Clinical professionals in obesity medicine may find chatbots valuable as a source of clinical and scientific knowledge, and for creating standard operating procedures, policies, and procedures. According to the findings, AI models can be used to identify clinically significant patterns of obesity or the connections between specific factors and weight outcomes. Moreover, the application of deep learning and machine learning approaches, such as logistic regression, decision trees, and artificial neural networks, appears to have yielded new insight into data, particularly in terms of obesity prediction. Conclusions: This work aims to contribute to a better understanding of obesity detection. While more studies are needed, AI offers solutions to modern challenges in obesity prediction.

The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Background and ObjectiveWith the wide application of electronic medical record systems in hospitals, massive medical data are available. This type of medical data has the characteristics of heterogeneity and multi-dimensionality. Traditional statistical methods cannot fully extract and use such data, but with their non-linear and cross-learning modes, machine-learning (ML) algorithms based on artificial intelligence can address these shortcomings. To explore the application of ML algorithms in the cardiovascular field, we retrieved and reviewed relevant articles published in the last 6 years and found that ML is practical and accurate in the auxiliary diagnosis of cardiovascular diseases. Thus, this article reviewed the research progress of ML in cardiovascular disease.MethodsThis study searched relevant literature published in National Center for Biotechnology Information (NCBI) PubMed from 2016 to 2022. The relevant literature was extracted from NCBI PubMed with the following keywords and their combinations: “machine learning”, “artificial intelligence”, “cardiology”, “cardiovascular disease”, “echocardiography”, “electrocardiogram” and “prediction model”. All articles included in the review are English.Key Content and FindingsThe review found that ML is practical and accurate in the diagnosis of cardiovascular diseases. Besides, ML can build clinical risk prediction models and help doctors evaluate the prognosis of patients.ConclusionsThe study summarized the progress of ML in cardiovascular diseases and confirmed its advantages in clinical application. In the future, models and software based on ML will be common auxiliary tools in clinical practice.

Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectively. Early diagnosis of CKD is an important step in preventing the progression of kidney damage. Our aim was to review publications on machine learning algorithms (MLAs) that can predict early CKD and itsprogression. We conducted a systematic review and selected 55 articles on the application of MLAs in CKD.PubMed, Medline, Scopus, Web of Science and IEEE Xplore Digital Library of the Institute of Electrical and Electronics Engineers were searched. The search terms were chronic kidney disease, artificial intelligence, data mining and machine learning algorithms. MLAs use enormous numbers of predictors combining them in non-linear and highly interactive ways. This ability increases when new data is added. We observed some limitations in the publications: (i) databases were not accurately reviewed by physicians; (ii) databases did not report the ethnicity of the patients; (iii) some databases collected variables that were not important for the diagnosis and progression of CKD; (iv) no information was presented on the native kidney disease causing CKD; (v) no validation of the results in external independent cohorts was provided; and (vi) no insights were given on the MLAs that were used. Overall, there was limited collaboration among experts in electronics, computer science and physicians. The application of MLAs in kidney diseases may enhance the ability of clinicians to predict CKD and RF, thus improving diagnostic assistance and providing suitable therapeutic decisions. However, it is necessary to improve the development process of MLA tools.