Comprehensive review of heart disease prediction: A comparative study from 2019 onwards

In recent decades, cardiovascular disease, or heart disease, has been the number one cause of death worldwide, establishing an urgent need for timely and accurate early diagnosis. The primary purpose of this review is to examine the current state of the art in heart disease prediction, addressing a shift from traditional diagnostic techniques to modern machine learning and deep learning methods, while maintaining a systematic and comprehensive approach. A critical review of the literature is conducted to assess the effectiveness and limitations of various predictive algorithms. This approach provides historical context, highlights outstanding research needs, and presents recent advancements. The review provides a comprehensive assessment of the challenges in predicting heart disease, which includes both the identification of specific risk factors and non-linear interactions between selected factors. The study also examines how the relationship between CVDs and kidney stones can influence the development of predictive models in the future. In conclusion, this study summarizes its key findings in a defined roadmap for future research, emphasizing the potential benefits of applying deep learning methods to enhance diagnostic precision and thus optimize patient management and outcomes.