Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment

The primary aim of this paper is to comprehend, assess, and analyze the role, relevance, and efficiency of machine learning models in predicting heart disease risks using clinical data. While the importance of heart disease risk prediction cannot be overstated, the application of machine learning (ML) in identifying and evaluating the impact of various features on the classification of patients with and without heart disease, as well as in generating a reliable clinical dataset, is equally significant. This study relies primarily on cross-sectional clinical data. The ML approach is designed to enhance the consideration of various clinical features in the heart disease prognosis process. Some features emerge as strong predictors, adding significant value. The paper evaluates seven ML classifiers: Logistic Regression, Random Forest, Decision Tree, Naive Bayes, k-Nearest Neighbors, Neural Networks, and Support Vector Machine (SVM). The performance of each model is assessed based on accuracy metrics. Notably, the Support Vector Machine (SVM) demonstrates the highest accuracy at 91.51%, confirming its superiority among the evaluated models in terms of predictive capability. The overall findings of this research highlight the advantages of advanced computational methodologies in the evaluation, prediction, improvement, and management of cardiovascular risks. In other words, the strong performance of the SVM model illustrates its applicability and value in clinical settings, paving the way for further advancements in personalized medicine and healthcare.

A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments

This paper presents a theoretical framework for an AI-driven data quality monitoring system designed to address the challenges of maintaining data quality in high-volume environments. We examine the limitations of traditional methods in managing the scale, velocity, and variety of big data and propose a conceptual approach leveraging advanced machine learning techniques. Our framework outlines a system architecture that incorporates anomaly detection, classification, and predictive analytics for real-time, scalable data quality management. Key components include an intelligent data ingestion layer, adaptive preprocessing mechanisms, context-aware feature extraction, and AI-based quality assessment modules. A continuous learning paradigm is central to our framework, ensuring adaptability to evolving data patterns and quality requirements. We also address implications for scalability, privacy, and integration within existing data ecosystems. While practical results are not provided, it lays a robust theoretical foundation for future research and implementations, advancing data quality management and encouraging the exploration of AI-driven solutions in dynamic environments.