AI DRIVEN RABBIT OPTIMIZER FOR EMERGENCY DEPARTMENT SURVEILLANCE AND MEDICAL DATA ANALYSIS
Keywords:
Medical data classification, emergency departments, KSA hospitals, feature selection, machine learning.Abstract
Amid the escalating prevalence of heart diseases globally, this project addresses the urgent need for more
accurate prediction methods. Heart-related conditions are on the rise, necessitating advanced tools for early
detection and intervention. The significance of this project lies in its potential to revolutionize heart disease
prediction. Accurate models can significantly impact public health by enabling timely and targeted interventions,
thereby mitigating the growing burden of cardiovascular illnesses. Focused on enhancing heart disease prediction,
the project employs state-of-the-art machine learning algorithms and feature engineering techniques. By assessing
models like ARO with VNBLR, Neural Network, Decision Tree, SVM, GBDT, and Naive Bayes, we aim to identify
the most effective approach for accurate predictions. This project benefits healthcare professionals, researchers, and
individuals concerned about heart health. Accurate prediction models provide a proactive approach to healthcare,
enabling personalized interventions and improving outcomes for individuals at risk of heart diseases. With heart
diseases becoming a leading cause of mortality worldwide, the outcomes of this project have the potential to
contribute significantly to the global health landscape. Timely and accurate predictions empower healthcare systems
to address the growing challenges posed by cardiovascular conditions. As an extension to the project, we have
incorporated advanced ensemble learning techniques to enhance the predictive capabilities of our heart disease
prediction model. Utilizing a stacking classifier, composed of Random Forest and Decision Tree classifiers, along
with a final LightGBM classifier, enables the model to harness the strengths of different algorithms for improved
accuracy. Additionally, a voting classifier, combining AdaBoost and Random Forest classifiers, enhances model
robustness through a soft voting mechanism.
