The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively.

Cloud computing adoption in e-government offers significant benefits yet presents unique challenges, particularly for developing countries. While existing research has focused on technical aspects, this study expands the analysis to investigate broader determinants of cloud adoption from technological, organizational, environmental, and individual perspectives. A systematic literature review was conducted across six scholarly databases and identified 40 primary studies. The findings emphasize the importance of adopting an integrated organizational-individual perspective to understand cloud adoption better. The study proposes a multi-dimensional conceptual framework for cloud adoption in government organizations and recommends further empirical validation of this model. This review provides decision-makers and managers in the government sector with practical insights for addressing adoption challenges, enhancing service delivery, and increasing cloud computing acceptance rate within government organizations.

Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.

Cardiovascular diseases (CVDs) remain the leading cause of death globally, accounting for approximately 17.9 million deaths each year. While diagnostic technologies have advanced, traditional methods often fail to detect early-stage heart disease, particularly in asymptomatic individuals. This study presents an intelligent, interpretable machine learning framework designed to enhance early CVD prediction. guided by domain-specific medical knowledge. Two well-established datasets the Kaggle Cardiovascular Disease Dataset and the Framingham Heart Study Dataset were used for model training and evaluation. Machine learning models including Random Forest, Support Vector Machine, and neural networks were applied, along with SMOTE-Tomek Links to address class imbalance. A score-specific weighted soft voting mechanism was used to improve prediction across varying risk categories. The final model achieved high performance: Score 1 (low risk) reached 97.4% accuracy and 0.99% AUC; Score 2 (moderate risk) achieved 83.6% accuracy and 0.90% AUC; Score 3 (high risk) yielded 93% accuracy and 0.97% AUC. These results demonstrate the model's high precision, recall, and generalizability across diverse patient profiles. By combining ensemble learning, medical knowledge, and advanced risk stratification, the framework offers a clinically relevant tool for early detection and personalized intervention, supporting proactive cardiovascular healthcare.