Optimized Cardiac and Circulatory Disorders Prediction: A High-Accuracy Model Selection Approach that utilizes Machine Learning and Deep Learning.

Cardiac and Circulatory Disorders are among the primary causes of death globally, making early detection crucial for successful treatment and prevention, This research presents an advanced predictive cardiac and circulatory Disorders risk assessment model using machine learning and deep learning techniques. Three different datasets were analyzed, and the most accurate dataset was selected to train the models. Various algorithms, including Decision Tree, XGBoost(Extreme Gradient Boosting), Ada-Boost, Multi-Layer Perceptron (MLP), and Tabnet were implemented and compared. The MLP model attained the highest accuracy of 98% among all evaluated approaches, surpassing traditional machine learning models. The proposed system is designed to support healthcare professionals in identifying at-risk patients early, enabling timely intervention and improved healthcare results.