AI Models for Early Detection and Mortality Prediction in Cardiovascular
Diseases
Abstract
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.