heart.csv

Abstract- Cardiovascular diseases (CVDs) remain a sig- nificant global health challenge, emphasizing the critical need for accurate predictive models to address early detec- tion and intervention. This study presents a comprehensive framework for heart disease prediction using advanced ma- chine learning techniques.

Background: CVDs are a leading cause of mortality worldwide, with early detection being crucial for effective treatment. Machine learning has emerged as a vital tool in healthcare due to its potential to enhance prediction accuracy. This study addresses the pressing need for accurate predictive models to combat CVDs, taking into account the existing challenges in the field.

Objective: The primary objective of this research is to develop a robust prediction model for Major Adverse Cardiovascular and Cerebrovascular Events (MACCE), a key indicator in evaluating coronary heart disease surgery’s success. The study leverages machine learning, focusing on feature selection, data balancing, and ensemble learning techniques.

Dataset Details: The study utilizes a real-world dataset comprising 303 samples and 13 features, derived from actual pathological data from cardiac patients. This dataset spans multiple years of return visits, providing valuable insights into the predictive capabilities of the model.

Model Validations: To ensure the model’s reliability, rig- orous validation techniques, including cross-validation, were employed. The dataset was carefully partitioned into training and testing sets, with the model achieving an accuracy of 87% in logistic regression, 95% in XGBoost, 83% in decision tree, and 90% in random forest, randomized search CV random forest, and grid search XGBoost, and 91% in the ensemble model. And after making sophisticated model the user interface platform leverage the AI algorithm and shown impressive accuracy 97 percent. Fig. 2 said so.

Comparison to Previous Works: This research contributes to the existing body of knowledge by proposing an innova- tive predictive model for heart disease. While comparing with previous methodologies, our approach demonstrates significant improvements in accuracy and effectiveness.

Clinical Implications: The developed model holds sub- stantial promise for clinical applications, aiding healthcare practitioners in early detection and risk assessment for heart diseases. The model’s implementation in real-world clinical settings has the potential to improve patient outcomes and reduce the burden of CVDs.

Limitations and Future Work: The study acknowledges potential limitations and emphasizes the need for further re-

search to address these challenges. Future work may involve exploring additional techniques, expanding the dataset, and conducting clinical trials for practical deployment.

Conclusion: In conclusion, this research represents a significant step forward in the field of CVD prediction. The developed model showcases impressive accuracy and holds promise for clinical use. It underscores the vital role of machine learning in addressing the global challenge of cardiovascular diseases, with potential implications for improved patient care and outcomes.